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Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays.

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Python · 1,693 lines · 58 KB · no license

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Mon Jan 30 16:16:17 2023
  5. @author: Francesco Mainardi
  6. """
  7. from scipy import stats
  8. import customtkinter
  9. import numpy as np
  10. import pandas as pd
  11. import h5py
  12. import matplotlib.pyplot as plt
  13. import os
  14. from tkinter import PhotoImage
  15. import glob
  16. from scipy.signal import butter, filtfilt
  17. import json
  18. from tkinter import filedialog
  19. from tkinter import Tk
  20. from tkinter.filedialog import askdirectory
  21. import subprocess
  22. import pickle
  23. import matplotlib.cm as cm
  25. df=pd.read_csv(r'channels ids table.csv', header=None,sep=';')
  26. # df2=pd.DataFrame(np.zeros((1,64)))
  27. # df=pd.concat([df, df2])
  28. # df.insert(64, int(64), np.zeros(65))
  29. # df=df.shift(periods=1)
  30. # df=df.shift(periods=1, axis='columns')
  32. conditions='Y'
  33. fpathDATA =''
  34. fnBRW =''
  35. fnBXR=''
  36. saving_path=''
  37. dfPSTH=0
  38. time_window_total=0
  39. blind_time_ms=0
  40. lfpduration_ms=0
  41. N2A_N2B_resolution_ms=0
  42. stim_duration_ms=0
  43. precisionvalue_ms=1
  44. lowpassvalue=0
  45. min_peak=0
  46. max_peak=0
  47. min_peak_distance_ms=0
  48. time_sec=0
  49. check_time_ms=0
  50. time_window_division_sec=1
  51. i=0
  52. ROI=[]
  53. ROIstr=[]
  54. ID=0
  55. freqvalue=0
  56. stimtimes=0
  57. map_mea = np.zeros((64, 64))
  58. b_map=[]
  59. c_map=[]
  60. bin_size=0
  61. ISI_bin_size=0
  62. bin_max=0
  63. pre_stimulus=0
  64. post_stimulus=0
  65. num_permutations=0
  66. total_time=0
  67. tart_frames=np.zeros(10000)
  68. P_Values=np.zeros(len(ROI))
  69. BRpath=""
  70. ROIpath=""
  71. save_folder=""
  72. BXR=""
  73. BRW=""
  74. localize_array=np.zeros(4096)
  75. customtkinter.set_appearance_mode("dark")
  76. customtkinter.set_default_color_theme("dark-blue")
  79. root= customtkinter.CTk(className="Python Analysis software for HD-MEA", fg_color="black")
  80. root.geometry("1890x920")
  81. root.title("Python Analysis software for HD-MEA")
  82. root.resizable(width=False, height=False)
  83. #img_bg=PhotoImage(file="python.png")
  87. #labelb= customtkinter.CTkLabel(master=root, width=250, height=250,text="", image=img_bg, fg_color="transparent")
  88. #labelb.place(x=725, y=315)
  91. def set_nan_in_lfp_matrix(lfp_matrix, lfp_N2A):
  92. for i in range(lfp_matrix.shape[0]):
  93. if np.isnan(lfp_N2A[i]):
  94. lfp_matrix[i, :] = np.nan
  95. return lfp_matrix
  98. def testprint():
  99. print(localize_array)
  101. def apri_image_processing():
  102. global localize_array
  103. subprocess.call(["python", "ImageProcessing.py"])
  104. with open('array.dat', 'rb') as file:
  105. tag_array = pickle.load(file)
  106. localize_array=tag_array
  107. df
  109. def apri_concatenator():
  111. subprocess.call(["python", "concatenator_new_1.3_GUI.py"])
  113. def on_closing():
  115. root.destroy()
  116. root.quit()
  119. def browse_ROI():
  120. global ROIpath
  122. loadROIpath.delete(0,1000)
  123. ROIpath = filedialog.askopenfilename()
  125. loadROIpath.insert(0, ROIpath)
  127. def browse_BRW():
  128. global BRW
  130. pacefname.delete(0,1000)
  131. BRW = filedialog.askopenfilename()
  133. pacefname.insert(0, BRW)
  135. def browse_BXR():
  136. global BXR
  138. fnameBXR.delete(0,1000)
  139. BXR = filedialog.askopenfilename()
  141. fnameBXR.insert(0, BXR)
  144. def browse_save():
  145. global save_folder
  147. savepacepath.delete(0,1000)
  148. root = Tk()
  149. root.withdraw()
  150. save_folder = askdirectory()
  152. savepacepath.insert(0, save_folder)
  156. def converti_coordinate_in_id(riga, colonna, num_colonne):
  157. return (riga - 1) * num_colonne + (colonna - 1)
  160. def estrai_riga_colonna_da_json(file_json):
  161. righe = []
  162. colonne = []
  164. # Carica il file JSON
  165. with open(file_json) as f:
  166. data = json.load(f)
  168. # Estrai le righe e le colonne di ogni elemento
  169. for group in data['ChsGroups']:
  170. for elemento in group['Chs']:
  171. righe.append(elemento['Row'])
  172. colonne.append(elemento['Col'])
  174. return righe, colonne
  176. def ROI_from_ROI():
  177. righe, colonne = estrai_riga_colonna_da_json(loadROIpath.get())
  178. global ROI
  179. for i in range(len(righe)):
  180. ROI.append(converti_coordinate_in_id(righe[i], colonne[i], 64))
  181. ids.delete(0,1000)
  183. global ROIstr
  185. stringID=str(ROI[i])
  186. ROIstr.append('['+stringID+']')
  187. ids.insert(0, ROIstr)
  188. global map_mea
  189. global b_map
  190. global c_map
  191. num1=righe[i]
  192. num2=colonne[i]
  193. b_map.append(num1-1)
  194. c_map.append(num2-1)
  195. map_mea[b_map, c_map]=100
  196. plt.imshow(map_mea, cmap='brg', interpolation='nearest')
  197. plt.savefig('img.png', bbox_inches='tight', transparent=True, dpi=60)
  198. img_map=PhotoImage(file="img.png")
  199. frame = customtkinter.CTkFrame(master=root, width=120, height=120, fg_color="transparent")
  200. frame.place(x=1040, y=0)
  201. label= customtkinter.CTkLabel(master=frame, width=120, height=120, text="", image=img_map)
  202. label.pack(pady=2, padx=1)
  206. def add_to_list ():
  207. ids.delete(0,1000)
  208. global ROI
  209. global ROIstr
  210. ROI.append(ID)
  211. stringID=str(ID)
  212. ROIstr.append('['+stringID+']')
  213. ids.insert(0, ROIstr)
  214. global map_mea
  215. global b_map
  216. global c_map
  217. num1=int(entry1.get())
  218. num2=int(entry2.get())
  219. b_map.append(num1-1)
  220. c_map.append(num2-1)
  221. map_mea[b_map, c_map]=100
  222. plt.imshow(map_mea, cmap='brg', interpolation='nearest')
  223. plt.savefig('img.png', bbox_inches='tight', transparent=True, dpi=60)
  224. img_map=PhotoImage(file="img.png")
  225. frame = customtkinter.CTkFrame(master=root, width=120, height=120, fg_color="transparent")
  226. frame.place(x=1040, y=0)
  227. label= customtkinter.CTkLabel(master=frame, width=120, height=120, text="", image=img_map)
  228. label.pack(pady=2, padx=1)
  230. def ROI_clear ():
  231. global ROI
  232. ROI=[]
  233. global ROIstr
  234. ROIstr=[]
  235. global b_map
  236. global c_map
  237. global map_mea
  238. b_map=[]
  239. c_map=[]
  240. map_mea=np.zeros((64, 64))
  241. plt.imshow(map_mea, cmap='brg', interpolation='nearest')
  242. plt.savefig('img.png', bbox_inches='tight', transparent=True, dpi=60)
  243. img_map=PhotoImage(file="img.png")
  244. frame = customtkinter.CTkFrame(master=root, width=120, height=120, fg_color="transparent")
  245. frame.place(x=1040, y=0)
  246. label= customtkinter.CTkLabel(master=frame, width=120, height=120, text="", image=img_map)
  247. label.pack(pady=2, padx=1)
  248. ids.delete(0,1000)
  250. def assign_path_pace ():
  252. global fnBRW
  253. global saving_path
  254. global fnBXR
  257. fnBRW = str(pacefname.get())
  258. fnBXR = str(fnameBXR.get())
  259. saving_path = str(savepacepath.get())
  261. def conversion ():
  263. global ID
  264. RESULTS.delete(0, 10)
  265. num1=int(entry1.get())
  266. num2=int(entry2.get())
  267. #b_map.append(num1-1)
  268. #c_map.append(num2-1)
  270. ID=int(df.loc[num1, num2])
  271. RESULTS.insert(0, ID)
  274. def lfp_duration(value):
  275. VALUE.delete(0,25)
  276. global lfpduration_ms
  277. lfpduration_ms=value
  278. VALUE.insert(0, value)
  279. def N2resolution(value):
  280. VALUE1.delete(0,25)
  281. global N2A_N2B_resolution_ms
  282. N2A_N2B_resolution_ms=value
  283. VALUE1.insert(0, value)
  284. def blind_time(value):
  285. VALUE2.delete(0,25)
  286. global blind_time_ms
  287. blind_time_ms=round(value, 1)
  288. VALUE2.insert(0, round(value, 1))
  289. def lowpassfilter(value):
  290. VALUE3.delete(0,25)
  291. global lowpassvalue
  292. lowpassvalue=value
  293. VALUE3.insert(0, value)
  294. def set_freqvalue01():
  295. global freqvalue
  296. freqvalue=0.1
  297. def set_freqvalue6():
  298. global freqvalue
  299. freqvalue=6
  300. def set_freqvalue10():
  301. global freqvalue
  302. freqvalue=10
  303. def set_freqvalue20():
  304. global freqvalue
  305. freqvalue=20
  306. def set_freqvalue50():
  307. global freqvalue
  308. freqvalue=50
  309. def set_freqvalue100():
  310. global freqvalue
  311. freqvalue=100
  312. def set_stimvalue1():
  313. global stimtimes
  314. stimtimes=0
  315. def set_stimvalue5():
  316. global stimtimes
  317. stimtimes=5
  318. def set_stimvalue10():
  319. global stimtimes
  320. stimtimes=10
  325. def binvalue(value):
  327. VALUE5.delete(0,25)
  328. global bin_size
  329. bin_size=int(value)
  330. VALUE5.insert(0, value)
  332. def binvalue2(value):
  334. VALUE52.delete(0,25)
  335. global ISI_bin_size
  336. ISI_bin_size=int(value)
  337. VALUE52.insert(0, value)
  339. def bin_max_value(value):
  341. VALUE53.delete(0,25)
  342. global bin_max
  343. bin_max=int(value)
  344. VALUE53.insert(0, value)
  346. def total_time(value):
  348. VALUE54.delete(0,25)
  349. global total_time
  350. total_time=int(value)
  351. VALUE54.insert(0, value)
  353. def prevalue(value):
  354. VALUE6.delete(0,25)
  355. global pre_stimulus
  356. pre_stimulus=int(value)
  357. VALUE6.insert(0, value)
  358. def postvalue(value):
  359. VALUE7.delete(0,25)
  360. global post_stimulus
  361. post_stimulus=int(value)
  362. VALUE7.insert(0, value)
  363. def permvalue(value):
  364. VALUE8.delete(0,25)
  365. global num_permutations
  366. num_permutations=int(value)
  367. VALUE8.insert(0, value)
  369. def spont_activity():
  370. assign_path_pace()
  371. global fpathDATA
  372. global fnBRW
  373. global fnBXR
  374. global saving_path
  375. global ISI_bin_size
  376. global pre_stimulus
  377. global post_stimulus
  378. global num_permutations
  379. global dfPSTH
  380. global c
  381. global P_Values
  382. global time_window_total
  383. global localize_array
  384. global total_time_s
  385. global bin_max
  390. ROI.sort()
  396. plotfolder="ISI plots"
  397. plotpath=os.path.join(saving_path, plotfolder)
  398. os.mkdir(plotpath)
  401. f = h5py.File(fnBXR,mode='r')
  402. SpikeTimes=np.array(f['3BResults']['3BChEvents']['SpikeTimes'])
  403. SpikeChIDs=np.array(f['3BResults']['3BChEvents']['SpikeChIDs'])
  404. SpikeUnits=np.array(f['3BResults']['3BChEvents']['SpikeUnits'])
  406. ChEvents=[SpikeChIDs, SpikeTimes, SpikeUnits]
  407. df=pd.DataFrame(data=ChEvents)
  409. c=0
  410. limit = int(bin_max/ISI_bin_size)
  413. ISI3d = np.zeros((limit, int(len(ROI))))
  414. isi_bins = np.arange(0, bin_max, ISI_bin_size)
  415. ISIdf=pd.DataFrame(index=isi_bins)
  416. ISIdf.index.name='BIN (ms)'
  417. features_list=['AV. FREQ', 'MODE FREQ.', 'SEM', 'CV', 'CV2']
  418. ISIfeatures=pd.DataFrame(index=features_list)
  420. for _ in range(len(ROI)):
  421. valore_cercato = ROI[c]
  423. # Seleziona la prima riga
  424. prima_riga = df.iloc[0]
  426. # Filtra colonne mantenendo solo quella in cui compare il valore cercato
  427. colonna_mantenuta = prima_riga[prima_riga == valore_cercato]
  429. # Seleziona solo le colonne desiderate dal DataFrame originale
  430. df_filtrato = df[colonna_mantenuta.index]
  432. ultima_riga = df_filtrato.iloc[-1]
  434. # Filtra colonne mantenendo solo quella in cui compare il valore cercato
  435. colonna_mantenuta = ultima_riga[ultima_riga > 0]
  437. # Seleziona solo le colonne desiderate dal DataFrame originale
  438. df_filtrato = df_filtrato[colonna_mantenuta.index]
  442. ISI_values, features = calculate_isi_histogram(df_filtrato.iloc[1], ISI_bin_size, bin_max, valore_cercato, plotpath)
  443. dfISI=pd.DataFrame(data=ISI_values)
  444. dfISI = dfISI.rename(columns={0: 'bin', 1: 'probability'})
  445. dfISI.to_csv('%s/ISI_Channel_%s.csv'%(saving_path, ROI[c]), decimal=',', sep =';')
  447. array= ISI_values[:,1]
  449. if len(array) < (limit) :
  450. array=np.pad(array, (0, limit - len(array)), 'constant')
  451. else:
  452. array=array[:limit]
  454. ISI3d[:, c] = array
  455. ISIdf[ROI[c]]=array
  456. ISIfeatures[ROI[c]]=features
  457. c=c+1
  459. ISIdf.to_csv('%s/ISI_Master.csv'%(saving_path), decimal=',', sep =';')
  460. ISIfeatures.to_csv('%s/Units_Features.csv'%(saving_path), decimal=',', sep =';')
  462. i=0
  463. fig = plt.figure(figsize=(8, 6))
  464. ax = fig.add_subplot(111, projection='3d')
  467. #colors = np.tile(['r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple'], 10)[:100]
  468. #colors = plt.cm.tab20.colors[:100]
  471. num_colors = 300
  472. colors = [cm.hsv(i/num_colors) for i in range(num_colors)]
  473. color_hex = [cm.colors.to_hex(color) for color in colors]
  476. set_lim=len(ROI)
  477. #x = np.arange(0, limit)
  478. x=isi_bins
  481. ys = np.arange(len(ROI))
  482. z=np.zeros(len(x))
  483. dx=ISI_bin_size
  486. ax.set_ylim([0, set_lim])
  487. # Plottaggio delle barre
  488. for i in range(len(ys)):
  489. y=ys[i]
  490. dz = ISI3d[:, i]
  491. ax.bar3d(x, y, z, dx, 0.1, dz, color=colors[i])
  492. i=i+1
  495. ax.xaxis.pane.fill = False
  496. ax.yaxis.pane.fill = False
  497. ax.zaxis.pane.fill = False
  498. ax.set_zlim(0, 0.5)
  500. ax.set_zlabel('Probability density')
  501. plt.xlabel('ISI (ms)')
  502. plt.ylabel('Channel ID')
  503. plt.yticks(np.arange(len(ROI)), ROI, fontsize=3, rotation=-45)
  504. filename = "ISI_3D.png" # Il nome del file del grafico
  505. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  506. plt.savefig(output_path)
  507. plt.close()
  508. #plt.show()
  510. fig = plt.figure(figsize=(8, 6))
  511. matrice = np.arange(64*64).reshape(64, 64)
  512. coordinate_x, coordinate_y = trova_coordinate(matrice, ROI)
  513. plt.gca().invert_yaxis()
  515. for id, x, y, colore in zip(ROI, coordinate_x, coordinate_y, colors):
  516. plt.scatter(y, x, color=colore, label=f'ID: {id}', s=8)
  518. plt.xlim(0, 63) # Imposta i limiti dell'asse x
  519. plt.ylim(0, 63) # Imposta i limiti dell'asse y
  520. plt.gca().set_aspect('equal', adjustable='box')
  522. plt.gca().invert_yaxis() # Inverti l'orientamento dell'asse Y
  523. plt.title('ISI map')
  524. filename = "ISI_map.png" # Il nome del file del grafico
  525. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  526. plt.savefig(output_path)
  527. plt.close()
  528. #plt.show()
  530. def trova_coordinate(matrice, id_casuali):
  531. coordinate_x = []
  532. coordinate_y = []
  533. for id in id_casuali:
  534. indice = np.where(matrice == id)
  535. if len(indice[0]) > 0: # Verifica se l'ID è presente nella matrice
  536. coordinate_x.append(indice[0][0])
  537. coordinate_y.append(indice[1][0])
  538. return coordinate_x, coordinate_y
  542. def calculate_cv(data):
  543. # Calcola la deviazione standard dei dati
  544. std_dev = np.std(data)
  545. # Calcola la media dei dati
  546. mean = np.mean(data)
  547. # Calcola il coefficiente di variazione
  548. cv = std_dev / mean
  550. return cv
  552. def calculate_cv2(data):
  553. cv2_values = []
  554. for i in range(len(data) - 1):
  555. ISIn = data[i]
  556. ISIn_plus_1 = data[i + 1]
  557. numerator = 2 * abs(ISIn_plus_1 - ISIn)
  558. denominator = ISIn + ISIn_plus_1
  559. cv2_values.append(numerator / denominator)
  560. return np.average(cv2_values)
  562. def calculate_isi_histogram(spikes, bin_size, bin_max, channel_id, plotpath):
  564. global total_time
  565. spikes = spikes/17.8
  566. isi = np.diff(spikes)
  568. isi = [x for x in isi if x <= bin_max]
  571. sem=np.std(isi)
  574. cv=calculate_cv(isi)
  575. cv2=calculate_cv2(isi)
  576. # Crea un istogramma degli intervalli tra gli spike
  577. isi_bins = np.arange(0, np.max(isi), bin_size)
  578. isi_hist, _ = np.histogram(isi, bins=isi_bins)
  580. # Calcola la densità di probabilità dell'istogramma
  581. isi_pdf = isi_hist / (len(spikes)) #* bin_size)
  583. ind_mode=np.where(isi_pdf == max(isi_pdf))[0][0]
  584. isi_mode = isi_bins[ind_mode]
  585. modelimit=isi_mode+sem
  589. moda=1/((isi_bins[ind_mode])/1000)
  590. error=abs((1000/modelimit)-moda)
  594. moda=round(moda, 2)
  598. #calcola la frequenza di firing in modo brutale
  599. av_freq=len(spikes)/total_time
  600. av_freq=round(av_freq, 2)
  601. text_freq=str(moda)
  602. text_av= str (av_freq)
  603. text_cv=str(round(cv,2))
  604. text_cv2=str(round(cv2,2))
  605. text_sem=str(round(error, 2))
  606. # Crea un array di valori x per il grafico
  607. x = isi_bins[:-1] + bin_size / 2
  609. # Plotta l'istogramma
  610. plt.figure(figsize=(8, 6))
  611. plt.bar(x, isi_pdf, width=bin_size, color='black')
  612. plt.xlabel('ISI (ms)')
  613. plt.ylabel('Probability Density')
  614. plt.title('ISI ch. %s | Av. Freq. = %s ± %s Hz | Mode = %s Hz | CV = %s | CV2 = %s' % (channel_id,text_av, text_sem, text_freq, text_cv, text_cv2))
  615. #plt.text(120, 0.05, f'Freq: {av_freq:.3f} Hz', color='black')
  616. plt.xlim(0, bin_max)
  617. filename = "ISI_channel_%s.png" % (channel_id) # Il nome del file del grafico
  618. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  619. plt.savefig(output_path)
  620. plt.locator_params(axis='x', nbins=15)
  621. #plt.show()
  622. plt.close()
  623. feature=[av_freq, moda, error, cv, cv2]
  626. return np.column_stack((x, isi_pdf)), feature
  632. def pacemakers():
  633. assign_path_pace()
  634. global fpathDATA
  635. global fnBRW
  636. global fnBXR
  637. global saving_path
  638. global bin_size
  639. global pre_stimulus
  640. global post_stimulus
  641. global num_permutations
  642. global dfPSTH
  643. global c
  644. global P_Values
  645. global ROI
  646. global localize_array
  648. bin_edges = np.arange(-pre_stimulus, post_stimulus + bin_size/2, bin_size)
  649. median_index=int(len(bin_edges)/2)
  652. plotfolder="PSTH plots"
  653. plotpath=os.path.join(saving_path, plotfolder)
  654. os.mkdir(plotpath)
  656. f = h5py.File(fnBXR,mode='r')
  657. SpikeTimes=np.array(f['3BResults']['3BChEvents']['SpikeTimes'])
  658. SpikeChIDs=np.array(f['3BResults']['3BChEvents']['SpikeChIDs'])
  659. SpikeUnits=np.array(f['3BResults']['3BChEvents']['SpikeUnits'])
  661. ChEvents=[SpikeChIDs, SpikeTimes, SpikeUnits]
  662. df=pd.DataFrame(data=ChEvents)
  666. num_frequenze=int((pre_stimulus+post_stimulus)/bin_size)
  668. SR=f['3BRecInfo']['3BRecVars']['SamplingRate'][0]
  670. index=[]
  671. i=0
  672. ST=np.delete(SpikeTimes, index)
  673. SCID=np.delete(SpikeChIDs, index)
  676. timeArt()
  677. abs_tart_frames=((tart_frames-100)/4096)/int(SR/1000)
  680. ROI.sort()
  681. colonne=np.array(ROI)
  682. dfPSTH=pd.DataFrame(columns=colonne)
  683. P_Values=np.zeros(len(ROI))
  684. c=0
  686. # Inizializza la matrice 3D vuota
  687. psth3d = np.zeros((num_frequenze, int(len(ROI))))
  689. # Loop sui canali nella ROI
  690. for _ in range(len(ROI)):
  691. valore_cercato = ROI[c]
  693. # Seleziona la prima riga
  694. prima_riga = df.iloc[0]
  696. # Filtra colonne mantenendo solo quella in cui compare il valore cercato
  697. colonna_mantenuta = prima_riga[prima_riga == valore_cercato]
  699. # Seleziona solo le colonne desiderate dal DataFrame originale
  700. df_filtrato = df[colonna_mantenuta.index]
  702. ultima_riga = df_filtrato.iloc[-1]
  704. # Filtra colonne mantenendo solo quella in cui compare il valore cercato
  705. colonna_mantenuta = ultima_riga[ultima_riga > 0]
  707. # Seleziona solo le colonne desiderate dal DataFrame originale
  708. df_filtrato = df_filtrato[colonna_mantenuta.index]
  712. indexID = df_filtrato.iloc[1]
  713. indexID = indexID / int(SR/1000)
  715. indexID=delete_stim_spike(indexID, abs_tart_frames, bin_size)
  716. psth = PSTH4(indexID, abs_tart_frames, ROI[c], bin_size, pre_stimulus, post_stimulus, num_permutations, plotpath, median_index)
  717. psth3d[:, c] = psth
  718. c=c+1
  719. i=0
  722. i=0
  723. fig = plt.figure()
  724. ax = fig.add_subplot(111, projection='3d')
  726. colors = np.tile(['r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple'], 10)[:100]
  729. set_lim=len(ROI)
  730. x = bin_edges[:-1]
  731. ys = np.arange(len(ROI))
  732. z=np.zeros(len(bin_edges[:-1]))
  733. dx=bin_size
  735. ax.set_ylim([0, set_lim])
  736. # Plottaggio delle barre
  737. for i in range(len(ys)):
  738. y=ys[i]
  739. dz = psth3d[:, i]
  740. ax.bar3d(x, y, z, dx, 0.1, dz, color=colors[i])
  741. i=i+1
  743. plt.xlabel('Bins')
  744. plt.ylabel('Channel ID')
  745. plt.yticks(np.arange(len(ROI)), ROI, fontsize=7, rotation=-45)
  746. filename = "PSTH_3D.png" # Il nome del file del grafico
  747. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  748. plt.savefig(output_path)
  749. plt.show()
  752. repeatimes=2
  753. psth3dNx=np.repeat(psth3d, repeatimes, axis=1)
  755. i=0
  756. ticks = []
  757. tick_positions = []
  758. for i in range(len(ROI)):
  760. ticks.append(ROI[i])
  761. tick_positions.append((i*(repeatimes)+int(repeatimes/2)))
  762. plt.figure(figsize=(8, 6))
  763. plt.imshow(np.transpose(psth3dNx, (1, 0)), cmap='viridis', origin='upper', vmin=0, vmax=100)
  764. plt.xlabel('Time (ms)')
  765. labels=np.arange(len(psth3dNx[:,0])+1)
  766. labels=labels[:len(psth3dNx[:,0])+1:5]
  767. labels_bin = bin_edges[::5]
  768. plt.xticks(labels, labels_bin, fontsize=7)
  769. plt.ylabel('Channnel ID')
  770. plt.yticks(tick_positions, ticks, fontsize=7)
  771. plt.colorbar()
  772. filename = "PSTH_Heatmap.png" # Il nome del file del grafico
  773. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  774. plt.savefig(output_path)
  775. plt.show()
  777. #dfPSTH.loc[len(dfPSTH)] = P_Values
  778. dfPSTH.to_csv('%s/PSTH_P-values.csv'%(saving_path), index = True, decimal=',', sep=';')
  779. dftag = pd.DataFrame(columns=ROI)
  780. dftag.loc[0] = localize_array[ROI]
  781. dftag.to_csv('%s/ROI tagged.csv'%(saving_path), index = True, decimal=',', sep=';')
  788. def delete_stim_spike(spike_times, stim_times, bin_size):
  790. global stimtimes
  791. global freqvalue
  793. n_pulses=stimtimes
  794. stim_distance_ms=1000/freqvalue
  796. if n_pulses == 1:
  797. indices_to_remove = []
  798. for stim_time in stim_times:
  799. start_time = stim_time - 1
  800. end_time = stim_time + 1.3
  801. indices = np.where((spike_times >= start_time) & (spike_times <= end_time))[0]
  802. indices_to_remove.extend(indices)
  803. else:
  805. indices_to_remove = []
  806. for stim_time in stim_times:
  807. i=0
  808. for _ in range(n_pulses):
  809. start_time = stim_time + i*stim_distance_ms - 1
  810. end_time = stim_time + i * stim_distance_ms + 1.3
  811. indices = np.where((spike_times >= start_time) & (spike_times <= end_time))[0]
  812. indices_to_remove.extend(indices)
  813. i=i+1
  817. # rimuovi gli elementi di spike_times indicizzati dagli indici trovati
  818. spike_times = spike_times.drop(spike_times.index[indices_to_remove])
  819. return spike_times
  824. def PSTH4(ST, abs_tart_frames, channel_id, bin_size, pre_stimulus, post_stimulus, num_permutations, plotpath, median_index):
  825. global bin_edges
  826. global dfPSTH
  827. global c
  828. global P_Values
  829. global bin_edges
  830. global localize_array
  831. # Crea un array di bin
  832. bin_edges = np.arange(-pre_stimulus, post_stimulus + bin_size/2, bin_size)
  834. # Inizia un loop sugli stimoli
  835. num_stimuli = len(abs_tart_frames)
  836. psth = np.zeros_like(bin_edges[:-1])
  837. for i in range(num_stimuli):
  838. # Seleziona il periodo di tempo da analizzare
  839. start_time = abs_tart_frames[i] - pre_stimulus
  840. end_time = abs_tart_frames[i] + post_stimulus
  842. # Conta quanti spike ci sono in ogni bin
  843. data_to_analyze = ST[(ST >= start_time) & (ST <= end_time)]
  844. counts, _ = np.histogram(data_to_analyze - abs_tart_frames[i], bins=bin_edges)
  845. psth += counts
  847. # Normalizza il PSTH dividendo per il numero di stimoli
  848. #psth = psth / num_stimuli
  849. psth = psth / num_stimuli / bin_size * 1000
  851. # Calcola la media della frequenza di scarica pre e post stimolo
  852. bin_removed_index = int(len(bin_edges) / 2)
  853. pre_stimulus_fr = np.mean(psth[:bin_removed_index])
  854. pre_stimulus_std = np.std(psth[:bin_removed_index])
  855. post_stimulus_fr = np.mean(psth[bin_removed_index+1:])
  857. post_stimulus_z_scores = (psth[bin_removed_index+1:] - pre_stimulus_fr) / pre_stimulus_std
  858. sig_bars = np.where(post_stimulus_z_scores > 2)[0]
  860. # Concatena i dati per il test delle permutazioni
  861. data = np.concatenate([psth[:bin_removed_index], psth[bin_removed_index+1:]])
  862. psth[median_index]=psth[median_index]*(bin_size/(bin_size-1.3))
  863. # Esegui il test delle permutazioni
  864. perm_fr_diffs = np.zeros(num_permutations)
  865. for i in range(num_permutations):
  866. np.random.shuffle(data)
  867. perm_pre_fr = np.mean(data[:bin_removed_index])
  868. perm_post_fr = np.mean(data[bin_removed_index:])
  869. perm_fr_diffs[i] = perm_post_fr - perm_pre_fr
  871. # Calcola il p-value
  872. observed_fr_diff = post_stimulus_fr - pre_stimulus_fr
  873. p_value = (np.sum(perm_fr_diffs >= observed_fr_diff) + 1) / (num_permutations + 1)
  874. print(f'p-value: {p_value}')
  877. # Imposta il colore di base di tutti i bin a blu
  878. bar_color = 'black'
  879. colors = [bar_color] * len(bin_edges[:-1])
  881. # Imposta il colore del bin dello stimolo su giallo
  882. stim_color = 'black'
  883. stim_bin = int(pre_stimulus / bin_size) # indice del bin corrispondente allo stimolo
  884. colors[stim_bin] = stim_color
  885. #psth[stim_bin]=0 #riduzione valore per migliorare l'impatto visivo
  886. plt.figure(figsize=(8, 6))
  887. # Visualizza il PSTH
  888. plt.bar(bin_edges[:-1], psth, width=bin_size, color=colors, align='edge')
  890. # Calcola la media e la deviazione standard del pre-stimolo
  891. pre_stimulus_data = data[:bin_removed_index]
  892. pre_stimulus_mean = np.mean(pre_stimulus_data)
  893. pre_stimulus_std = np.std(pre_stimulus_data)
  895. # Aggiungi l'asterisco sopra le barre significative del post-stimolo
  896. sig_bars = []
  897. for i, post_stimulus_fr in enumerate(psth[bin_removed_index:]):
  898. z_score = (post_stimulus_fr - pre_stimulus_mean) / pre_stimulus_std
  899. if z_score > 1.645 or z_score < -1.645: # 95% di confidenza a 2 code
  900. sig_bars.append(i)
  901. plt.text(bin_edges[bin_removed_index+1+i]-bin_size/2, post_stimulus_fr, '*', color='red', ha='center', va='bottom')
  903. # Imposta l'etichetta delle barre significative
  906. plt.xlabel('Time (ms)')
  907. plt.ylabel('Firing rate (Hz)')
  908. plt.title('Peri-Stimulus Time Histogram channel %s [ %s ]' % (channel_id, localize_array[channel_id]))
  909. #plt.text(10, 120, f'permutational test p-value: {p_value:.3f}', color='black')
  911. # Imposta i limiti dell'asse x e y in modo che lo stimolo sia al centro del grafico e l'asse y sia congruo
  912. plt.xlim([-pre_stimulus, post_stimulus])
  913. plt.ylim(bottom=0, top=150)
  914. plt.locator_params(axis='x', nbins=20)
  915. plt.axvline(x=0, color='yellow', linestyle='--')
  916. plt.tight_layout()
  917. filename = "PSTH_channel_%s.png" % (ROI[c]) # Il nome del file del grafico
  918. output_path = os.path.join(plotpath, filename) # Percorso completo del file di output
  919. plt.savefig(output_path)
  920. plt.show()
  921. dfPSTH[ROI[c]] = psth
  922. P_Values[c] = p_value
  924. return psth
  929. def timeArt():
  932. global stimtimes
  933. global freqvalue
  934. #directory of .brw file
  935. global fnBRW
  936. global precision
  938. f = h5py.File(fnBRW,mode='r')
  939. MinVolt=float(f['3BRecInfo']['3BRecVars']['MinVolt'][0])
  940. MaxVolt=float(f['3BRecInfo']['3BRecVars']['MaxVolt'][0])
  941. BitDepth=f['3BRecInfo']['3BRecVars']['BitDepth'][0]
  942. SignalInversion=float(f['3BRecInfo']['3BRecVars']['SignalInversion'][0])
  943. SR=f['3BRecInfo']['3BRecVars']['SamplingRate'][0]
  944. rawdata = (f['3BData']['Raw'])
  945. chid=100
  948. stim_duration_ms=1
  951. precision=int(stim_duration_ms)
  954. precision_frames=int(((200)*SR)/1000)
  955. frame=np.zeros(100000000)
  956. n_artifacts=10000
  957. tart=np.zeros(n_artifacts)
  959. global tart_frames
  962. k=0
  963. j=1
  965. t=0
  967. for i in range(chid, len(rawdata), 4096):
  968. ADCCountsToMV = SignalInversion * ((MaxVolt-MinVolt)/2**BitDepth)
  969. offset=SignalInversion*MinVolt
  970. AnalogValue = offset + rawdata[i] *ADCCountsToMV
  971. if AnalogValue > 1500:
  972. frame[j]=(i-chid)/4096
  973. if (frame[j]-frame [j-1])>precision_frames:
  974. sec = ((i-chid)/4096)/SR
  975. msec = sec
  976. print(k,') ', 'sec=', round(msec, 2) ,'peak=', AnalogValue)
  977. tart[t]=msec
  978. tart_frames[t]=i
  979. k=k+1
  980. j=j+1
  981. t=t+1
  982. else:
  983. j=j+1
  986. resize_factor=t
  987. tart_frames=np.resize(tart_frames, resize_factor)
  994. def lowpass_filter(trace, SR):
  995. global lowpassvalue
  996. freq_sampling = SR
  997. # Applicazione del filtro lowpass a 2000 Hz
  998. cutoff_hz = lowpassvalue # frequenza di taglio in Hz
  999. order = 4 # ordine del filtro
  1000. nyquist_hz = 0.5 * freq_sampling # frequenza di Nyquist
  1001. cutoff_norm = cutoff_hz / nyquist_hz # frequenza di taglio normalizzata
  1002. b, a = butter(order, cutoff_norm, btype='lowpass', analog=False)
  1004. filtered_trace = filtfilt(b, a, trace)
  1005. return filtered_trace
  1009. def LFP_analysis():
  1012. directory1='N2A'
  1013. directory2='N2B'
  1014. plotfolder='LFPs Plots'
  1018. #directories of .brw file
  1019. assign_path_pace()
  1020. global fpathDATA
  1021. global fnBRW
  1022. global saving_path
  1023. global stimtimes
  1024. global freqvalue
  1025. global localize_array
  1026. precisionvalue_ms=5
  1027. global localize_array
  1030. path1 = os.path.join(saving_path, directory1)
  1031. path2 = os.path.join(saving_path, directory2)
  1032. path3 = os.path.join(saving_path, plotfolder)
  1034. os.mkdir(path1)
  1035. os.mkdir(path2)
  1036. os.mkdir(path3)
  1038. #data extraction from .brw file
  1039. f = h5py.File(fnBRW,mode='r')
  1040. MinVolt=float(f['3BRecInfo']['3BRecVars']['MinVolt'][0])
  1041. MaxVolt=float(f['3BRecInfo']['3BRecVars']['MaxVolt'][0])
  1042. BitDepth=f['3BRecInfo']['3BRecVars']['BitDepth'][0]
  1043. SignalInversion=float(f['3BRecInfo']['3BRecVars']['SignalInversion'][0])
  1044. SR=f['3BRecInfo']['3BRecVars']['SamplingRate'][0]
  1045. rawdata = (f['3BData']['Raw'])
  1047. #params and initializatio (k,j,h, are counters), chid is channel id for detection and the precision is the
  1048. #frame interval that the script use to detect the same time artifact in differents frames
  1052. frame=np.zeros(100000)
  1053. n_artifacts=10000
  1054. tart=np.zeros(n_artifacts)
  1055. tart_frames=np.zeros(n_artifacts)
  1056. blind_time_ms
  1057. lfpduration_ms
  1058. N2A_N2B_resolution_ms
  1059. # if stimtimes == 0 :
  1060. # stim_duration_ms=0
  1061. # else:
  1062. # stimduration_tot=((stimtimes/freqvalue)*1000)
  1063. # stim_duration_ms=stimduration_tot - stimduration_tot/stimtimes
  1066. k=0
  1067. j=1
  1069. t=0
  1070. c=0
  1071. a=0
  1072. precision_frames=int((precisionvalue_ms*SR)/1000)
  1073. blind_frames=int((blind_time_ms*SR)/1000)
  1074. lfpduration_frames=int((lfpduration_ms*SR)/1000)
  1075. N2A_N2B_resolution=int((N2A_N2B_resolution_ms*SR)/1000)
  1080. ROI
  1081. ROI.sort()
  1082. limit=len(ROI)
  1083. chid=ROI[0]
  1085. #the script extract 1 values every 4096 (due to the matrix organization)
  1086. #to extract one single trace corresponding for a single channel
  1087. #then convert the digital data in analog data (mV) using the 3Brain formula
  1088. #for conversion and when the script find a maxima print the value and the time in ms.
  1089. #to avoid to detect the same time artifact in two adjecent frames the script compare each
  1090. #values with the previous one, and if the difference in frames is under the precision value does not consider it
  1091. #for stimulation different by the 0.1 Hz (6 Hz, 20 Hz, ...), change the precision value (calculate how many frames
  1092. #correspond to ms between two stimuli) to extract the last stimuli
  1094. #stim artifact detection
  1095. for i in range(chid, len(rawdata), 4096):
  1096. ADCCountsToMV = SignalInversion * ((MaxVolt-MinVolt)/2**BitDepth)
  1097. offset=SignalInversion*MinVolt
  1098. AnalogValue = offset + rawdata[i] *ADCCountsToMV
  1099. if AnalogValue > 1500:
  1100. frame[j]=(i-chid)/4096
  1101. if (frame[j]-frame [j-1])>precision_frames:
  1102. sec = ((i-chid)/4096)/SR
  1103. msec = sec
  1104. print(k,') ', 'sec=', round(msec, 2) ,'peak=', AnalogValue)
  1105. tart[t]=msec
  1106. tart_frames[t]=i
  1107. k=k+1
  1108. j=j+1
  1109. t=t+1
  1110. else:
  1111. j=j+1
  1113. resize_factor=t
  1114. lfptraces=np.zeros(resize_factor)
  1115. lfp_N2A=np.zeros(resize_factor)
  1116. lfp_N2B=np.zeros(resize_factor)
  1117. tart=np.resize(tart, resize_factor)
  1118. tart_frames=np.resize(tart_frames, resize_factor)
  1119. posizioniN2A=np.zeros(resize_factor)
  1120. posizioniN2B=np.zeros(resize_factor)
  1121. lfp_array=np.zeros(lfpduration_frames)
  1122. lfp_matrix=np.zeros((len(lfptraces),lfpduration_frames))
  1123. #peaks_array=[]
  1124. k=0
  1126. #peaks
  1127. #N2peaks=np.zeros((resize_factor,2))
  1129. k=0
  1130. j=1
  1132. t=0
  1133. g=0
  1134. i=0
  1136. for _ in ROI:
  1137. chid=ROI[c]
  1138. for i in tart_frames:
  1139. t=0
  1140. g=0
  1141. for _ in range(lfpduration_frames):
  1142. ADCCountsToMV = SignalInversion * ((MaxVolt-MinVolt)/2**BitDepth)
  1143. offset=SignalInversion*MinVolt
  1144. index=int(tart_frames[k]+(blind_frames+g)*4096)
  1145. AnalogValue = offset + rawdata[index] *ADCCountsToMV
  1146. lfp_array[t]= AnalogValue
  1147. t=t+1
  1148. g=g+1
  1149. #lfp_matrix[k,:]=lowpass_filter(lfp_array, SR)
  1150. lfp_matrix[k,:]=lfp_array
  1153. k=k+1
  1156. i=0
  1158. #std_noise= [np.std(arr) for arr in lfp_matrix]
  1160. for _ in range(len(lfp_N2A)):
  1161. if len(lfp_matrix[i] > 0):
  1162. lfp_N2A[i]=min(lfp_matrix[i,0:N2A_N2B_resolution])
  1163. posizioniN2A[i] = np.where(lfp_matrix[i]==min(lfp_matrix[i,0:N2A_N2B_resolution]))[0][0]
  1164. i=i+1
  1165. else:
  1166. lfp_N2A[i] = None
  1167. posizioniN2A[i]=None
  1169. i=0
  1172. for i in range(len(lfp_N2A)):
  1173. if lfp_N2A[i] > (np.mean(lfp_matrix[i,-100:])-3*np.std(lfp_matrix[i,-100:])):
  1174. lfp_N2A[i] = np.nan
  1176. print(c , ')', ROI[c])
  1178. lfp_N2A=np.resize(lfp_N2A, resize_factor)
  1179. print('lfp N2A values:', lfp_N2A)
  1180. mean=np.nanmean(lfp_N2A)
  1181. sem=np.nanstd(lfp_N2A) / np.sqrt(np.sum(~np.isnan(lfp_N2A)))
  1182. print('lfp N2A avarege:', mean, '+- ', sem, 'pA')
  1186. #N2peaks=np.zeros((resize_factor,2))
  1187. i=0
  1188. j=0
  1190. for _ in range(len(lfp_N2B)):
  1192. start= int (posizioniN2A[i])+int(N2A_N2B_resolution)
  1193. if len(lfp_matrix[i, start:start+N2A_N2B_resolution]):
  1194. lfp_N2B[i]=min(lfp_matrix[i, start:start+N2A_N2B_resolution])
  1195. position = (np.where(lfp_matrix[i, start:start+N2A_N2B_resolution]== lfp_N2B[i])[0])+start
  1196. posizioniN2B[i]=position[0]
  1197. i=i+1
  1198. else:
  1199. lfp_N2B[i]=None
  1200. i=i+1
  1202. for i in range(len(lfp_N2B)):
  1203. if lfp_N2B[i] > (np.mean(lfp_matrix[i,-100:])-2*np.std(lfp_matrix[i,-100:])):
  1204. lfp_N2B[i] = np.nan
  1206. lfp_N2B=np.resize(lfp_N2B, resize_factor)
  1207. print('lfp N2B values:', lfp_N2B)
  1208. mean=np.nanmean(lfp_N2B)
  1209. sem=np.nanstd(lfp_N2B) / np.sqrt(np.sum(~np.isnan(lfp_N2B)))
  1210. print('lfp N2B avarege:', mean, '+- ', sem, 'pA')
  1213. for i in range(len(posizioniN2A)):
  1214. mean_value = np.nanmean(posizioniN2A)
  1215. if not (mean_value - 18 <= posizioniN2A[i] <= mean_value + 18):
  1216. posizioniN2A[i] = np.nan
  1217. posizioniN2B[i] = np.nan
  1218. lfp_N2A[i] = np.nan
  1219. lfp_N2B[i] = np.nan
  1222. consecutive_threshold = 5
  1223. value_threshold = 1000
  1224. i=0
  1225. # Iterare lungo l'asse 0 di lfp_matrix
  1226. for i in range(lfp_matrix.shape[0]):
  1227. consecutive_count = 0
  1229. # Iterare lungo l'asse 1 di lfp_matrix
  1230. for j in range(lfp_matrix.shape[1]):
  1231. if lfp_matrix[i, j] > value_threshold:
  1232. consecutive_count += 1
  1233. if consecutive_count >= consecutive_threshold:
  1234. # Impostare a NaN i valori corrispondenti in lfp_N2A e lfp_N2B
  1235. lfp_N2A[i] = np.nan
  1236. lfp_N2B[i] = np.nan
  1237. posizioniN2A[i]= np.nan
  1238. posizioniN2B[i]= np.nan
  1239. else:
  1240. consecutive_count = 0
  1241. i=0
  1244. for i in range(lfp_matrix.shape[0]):
  1245. if np.isnan(lfp_N2A[i]):
  1246. lfp_matrix[i, :] = np.nan
  1248. i=0
  1249. #plotting LFP
  1250. plt.figure(figsize=(8, 6))
  1251. for _ in range(len(lfp_matrix[:, 0:resize_factor])):
  1252. plt.plot(lfp_matrix[i, :], "black", alpha=0.1)
  1253. if not np.isnan(posizioniN2A[i]):
  1254. plt.plot(int(posizioniN2A[i]), lfp_matrix[i, int(posizioniN2A[i])], "o", color= "#686AAD", markersize=2)
  1255. if not np.isnan(posizioniN2B[i]):
  1256. plt.plot(int(posizioniN2B[i]), lfp_matrix[i, int(posizioniN2B[i])], "o", color= "orange", markersize=2)
  1257. i = i + 1
  1259. lfp_matrixav = set_nan_in_lfp_matrix(lfp_matrix, lfp_N2A)
  1261. lfp_mean=np.zeros(len(lfp_matrixav[1,:]))
  1263. i=0
  1264. #avarege lfp and avarage N2A and N2B finded, the last check
  1265. for _ in range(len(lfp_matrixav[1,:])):
  1266. lfp_mean[i]=np.nanmean(lfp_matrixav[:,i])
  1267. i=i+1
  1269. N2Amean_frame = np.nanmean(posizioniN2A)
  1270. if np.isnan(N2Amean_frame):
  1271. N2Amean_frame = 0
  1272. else:
  1273. N2Amean_frame = int(N2Amean_frame)
  1275. N2Bmean_frame = np.nanmean(posizioniN2B)
  1276. if np.isnan(N2Bmean_frame):
  1277. N2Bmean_frame = 0
  1278. else:
  1279. N2Bmean_frame = int(N2Bmean_frame)
  1281. plt.plot(lfp_mean, "black", alpha = 0.8)
  1283. if N2Amean_frame != 0:
  1284. plt.plot(N2Amean_frame, lfp_mean[N2Amean_frame], "o", color="#686AAD", markersize = 8)
  1285. if N2Bmean_frame != 0:
  1286. plt.plot(N2Bmean_frame, lfp_mean[N2Bmean_frame], "o", color = "orange", markersize = 8)
  1287. current_lower, current_upper = plt.gca().get_ylim()
  1288. plt.ylim(current_lower, 250)
  1289. plt.title('LFPs channel %s [%s]' % (ROI[c], localize_array[ROI[c]]))
  1290. plt.xlabel("Time (ms)")
  1291. plt.ylabel("Amplitude (μV)")
  1293. #x = range(len(lfp_matrix[0, :]))
  1294. #indices = [i // 18 for i in range(len(x))]
  1295. #[labels = [str(index) if i % 18 == 0 else '' for i, index in enumerate(indices)]
  1297. x = np.arange(0, len(lfp_matrix[0, :]), 18)
  1298. labels = np.arange(0,len(x))
  1299. labels = [str(val) for val in labels]
  1302. plt.xticks(x, labels)
  1303. filename = "LFPs_channel_%s.png" % (ROI[c]) # Il nome del file del grafico
  1304. output_path = os.path.join(path3, filename) # Percorso completo del file di output
  1305. plt.savefig(output_path)
  1306. #plt.show()
  1307. #savings
  1309. timeArt=pd.DataFrame(data=tart)
  1310. timeArt.to_csv('%s/timeArtifact_master.csv'%(saving_path), index = False, header=False, decimal=',')
  1311. N2A=pd.DataFrame(data=lfp_N2A)
  1312. N2ind=str(chid)
  1313. N2A.to_csv('%s/%s/N2A_amplitudes_%s.csv'%(saving_path, directory1, N2ind), index = False, header=False, decimal=',')
  1314. N2B=pd.DataFrame(data=lfp_N2B)
  1315. N2B.to_csv('%s/%s/N2B_amplitudes_%s.csv'%(saving_path,directory2, N2ind), index = False, header=False, decimal=',')
  1318. k=0
  1319. j=1
  1321. t=0
  1322. g=0
  1323. i=0
  1324. c=c+1
  1326. if a == (limit-1):
  1327. break
  1328. else:
  1329. tart_frames=tart_frames+(ROI[a+1]-ROI[a])
  1330. a=a+1
  1334. os.chdir(saving_path+'/'+directory1)
  1335. filenameG=[i for i in glob.glob('*.{}'.format("csv"))]
  1336. filenameG.sort(key=len)
  1337. dfTOTN2A = pd.concat([pd.read_csv(f, header=None) for f in filenameG], axis = 'columns')
  1338. dfTOTN2A.to_csv('%s/%s/N2A_master.csv'%(saving_path, directory1), index = False, header=ROI, decimal=',', sep =';')
  1340. os.chdir(saving_path+'/'+directory2)
  1341. filenameG2=[i for i in glob.glob('*.{}'.format("csv"))]
  1342. filenameG2.sort(key=len)
  1343. dfTOTN2B = pd.concat([pd.read_csv(f, header=None) for f in filenameG2], axis='columns')
  1344. dfTOTN2B.to_csv('%s/%s/N2B_master.csv'%(saving_path, directory2), index = False, header=ROI, decimal=',', sep=';')
  1346. prefix="N2A_amplitudes"
  1347. for filename in os.listdir(saving_path+"/N2A"):
  1348. if filename.startswith(prefix): # se il file inizia con il prefisso desiderato
  1349. file_path = os.path.join(saving_path +"/N2A", filename) # costruisci il percorso del file
  1350. os.remove(file_path) # elimina il file
  1352. prefix2="N2B_amplitudes"
  1353. for filename in os.listdir(saving_path+"/N2B"):
  1354. if filename.startswith(prefix2): # se il file inizia con il prefisso desiderato
  1355. file_path = os.path.join(saving_path +"/N2B", filename) # costruisci il percorso del file
  1356. os.remove(file_path) # elimina il file
  1360. os.chdir(saving_path)
  1377. #channel conversion GUI, ready
  1379. frame = customtkinter.CTkFrame(master=root, fg_color="transparent")
  1381. frame.place(x=10, y=0)
  1382. label= customtkinter.CTkLabel(master=frame, width=250, text="ID Conversion", font=("Arial Rounded MT Bold", 25), text_color='white')
  1383. label.pack(pady=2, padx=1)
  1384. entry1= customtkinter.CTkEntry(master=frame, width=250, placeholder_text="first value", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1385. entry1.pack(pady=2, padx=1, expand=True)
  1387. entry2= customtkinter.CTkEntry(master=frame, width=250, placeholder_text="second Value", font=("Arial Rounded MT Bold", 15), corner_radius=15)
  1388. entry2.pack(pady=2, padx=1)
  1390. button = customtkinter.CTkButton(master=frame, width=250, text="Conversion",font=("Arial Rounded MT Bold", 15), command=conversion,corner_radius=15 )
  1391. button.pack(pady=2, padx=1)
  1392. RESULTS = customtkinter.CTkEntry(master=frame, width=250, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1393. RESULTS.pack(pady=2, padx=1)
  1394. button_add = customtkinter.CTkButton(master=frame, width=250, text="Add to list",font=("Arial Rounded MT Bold", 15), command=add_to_list,corner_radius=15)
  1395. button_add.pack(pady=2, padx=1)
  1396. button_clear = customtkinter.CTkButton(master=frame, width=250, text="Clear ROI",font=("Arial Rounded MT Bold", 15), command=ROI_clear,corner_radius=15)
  1397. button_clear.pack(pady=2, padx=1)
  1401. #LFP analysis GUI, add parameters, function and ROI get
  1402. frame = customtkinter.CTkFrame(master=root, width=600, fg_color="transparent")
  1403. frame.place(x=10, y=280)
  1404. label= customtkinter.CTkLabel(master=frame, width=600, text="LFP Analysis", font=("Arial Rounded MT Bold", 25), text_color='white')
  1405. label.pack(pady=2, padx=1)
  1408. ######sliders##########
  1409. label= customtkinter.CTkLabel(master=frame, width=600, text="LFP extraction time (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1410. label.pack(pady=2, padx=1)
  1411. VALUE= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1412. VALUE.pack(pady=2, padx=1)
  1413. LFPduration= customtkinter.CTkSlider(master=frame, width=600, from_=0, to=25, number_of_steps=25, command=lfp_duration)
  1414. LFPduration.pack(pady=2, padx=2)
  1416. ####################
  1417. label1= customtkinter.CTkLabel(master=frame, width=600, text="N2A N2B time window (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1418. label1.pack(pady=2, padx=1)
  1419. VALUE1= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1420. VALUE1.pack(pady=2, padx=1)
  1421. resolution= customtkinter.CTkSlider(master=frame, width=600, from_=0, to=5, number_of_steps=10, command=N2resolution)
  1422. resolution.pack(pady=2, padx=1)
  1424. label2= customtkinter.CTkLabel(master=frame, width=600, text="blind time (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1425. label2.pack(pady=2, padx=1)
  1426. VALUE2= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1427. VALUE2.pack(pady=2, padx=1)
  1428. blind= customtkinter.CTkSlider(master=frame, width=600, from_=0, to=5, number_of_steps=50, command=blind_time)
  1429. blind.pack(pady=2, padx=1)
  1431. label3= customtkinter.CTkLabel(master=frame, width=600, text="low pass filter frequency, for noise (Hz)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1432. label3.pack(pady=2, padx=1)
  1433. VALUE3= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1434. VALUE3.pack(pady=2, padx=1)
  1435. lowpass= customtkinter.CTkSlider(master=frame, width=600, from_=1000, to=10000, number_of_steps=9, command=lowpassfilter)
  1436. lowpass.pack(pady=2, padx=1)
  1443. frame = customtkinter.CTkFrame(master=root, width=600, fg_color="transparent")
  1444. frame.place(x=10, y=740)
  1445. button = customtkinter.CTkButton(master=frame, width=600, height=70, text="START LFP ANALYSIS", font=("Arial Rounded MT Bold", 25), command=LFP_analysis,corner_radius=30)
  1446. button.pack(padx=1, pady=1)
  1450. frame = customtkinter.CTkFrame(master=root, fg_color='transparent')
  1451. frame.place(x=335, y=0)
  1452. label= customtkinter.CTkLabel(master=frame, width=600, text="File Path", font=("Arial Rounded MT Bold", 25), text_color='white')
  1453. label.pack(pady=2, padx=1)
  1454. pacefname= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="BRW file name with extension", font=("Arial Rounded MT Bold", 15), corner_radius=15)
  1455. pacefname.pack(pady=2, padx=1)
  1456. fnameBXR= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="BXR file name with extension", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1457. fnameBXR.pack(pady=2, padx=1)
  1458. savepacepath= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="save path", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1459. savepacepath.pack(pady=2, padx=1)
  1460. loadROIpath= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="ROI file path and name", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1461. loadROIpath.pack(pady=2, padx=1)
  1462. button_ROI = customtkinter.CTkButton(master=frame, width=600, text="Load ROI",font=("Arial Rounded MT Bold", 15), command=ROI_from_ROI,corner_radius=15)
  1463. button_ROI.pack(pady=2, padx=1)
  1464. # segmbutton = customtkinter.CTkButton(master=frame, width=600, text="Start image segmentation",font=("Arial Rounded MT Bold", 15), command=apri_image_processing,corner_radius=30)
  1465. # segmbutton.pack(pady=2, padx=1)
  1466. # segmbutton = customtkinter.CTkButton(master=frame, width=600, text="Concatenate multiple files",font=("Arial Rounded MT Bold", 15), command=apri_concatenator,corner_radius=30)
  1467. # segmbutton.pack(pady=2, padx=1)
  1471. frame = customtkinter.CTkFrame(master=root, width=1890)
  1472. frame.place(x=0, y=260)
  1473. progressbar_1 = customtkinter.CTkProgressBar(master=frame, width=1890, progress_color="#00FF00")
  1474. progressbar_1.pack(pady=2, padx=1)
  1475. progressbar_1.configure(mode="indeterminnate")
  1476. progressbar_1.start()
  1483. #pacemakers GUI, insert parameters, function and ROI get
  1484. frame = customtkinter.CTkFrame(master=root, fg_color='transparent')
  1485. frame.place(x=645, y=280)
  1486. label= customtkinter.CTkLabel(master=frame, width=600, text="Autorhythmic Cells Evoked Activity Analysis", font=("Arial Rounded MT Bold", 25), text_color='white')
  1487. label.pack(pady=2, padx=1)
  1489. savepacepath.pack(pady=2, padx=1)
  1493. label5= customtkinter.CTkLabel(master=frame, width=600, text="bin size (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1494. label5.pack(pady=2, padx=1)
  1495. VALUE5= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1496. VALUE5.pack(pady=2, padx=1)
  1497. binslider= customtkinter.CTkSlider(master=frame, width=600, from_=1, to=50, number_of_steps=49, command=binvalue)
  1498. binslider.pack(pady=2, padx=1)
  1500. label6= customtkinter.CTkLabel(master=frame, width=600, text="pre-stimulus time window (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1501. label6.pack(pady=2, padx=1)
  1502. VALUE6= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1503. VALUE6.pack(pady=2, padx=1)
  1504. preslider= customtkinter.CTkSlider(master=frame, width=600, from_=50, to=1200, number_of_steps=23, command=prevalue)
  1505. preslider.pack(pady=2, padx=1)
  1507. label7= customtkinter.CTkLabel(master=frame, width=600, text="post-stimulus time window (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1508. label7.pack(pady=2, padx=1)
  1509. VALUE7= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1510. VALUE7.pack(pady=2, padx=1)
  1511. postslider= customtkinter.CTkSlider(master=frame, width=600, from_=50, to=1200, number_of_steps=23, command=postvalue)
  1512. postslider.pack(pady=2, padx=1)
  1514. label8= customtkinter.CTkLabel(master=frame, width=600, text="num permutation for significance test", font=("Arial Rounded MT Bold", 15), text_color='white')
  1515. label8.pack(pady=2, padx=1)
  1516. VALUE8= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1517. VALUE8.pack(pady=2, padx=1)
  1518. permslider= customtkinter.CTkSlider(master=frame, width=600, from_=1000, to=10000, number_of_steps=9, command=permvalue)
  1519. permslider.pack(pady=2, padx=1)
  1521. label9= customtkinter.CTkLabel(master=frame, width=600, height=47, text="", font=("Arial Rounded MT Bold", 15), text_color='white')
  1522. label9.pack(pady=2, padx=1)
  1525. frameC = customtkinter.CTkFrame(master=root, width=600, fg_color="transparent")
  1526. frameC.place(x=645, y=680)
  1527. ################################ STRING 1000!!!!! ################################
  1528. checkbox01Hz = customtkinter.CTkCheckBox(master=frameC, text="0.1 Hz", command=set_freqvalue01,
  1529. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1531. checkbox01Hz.grid(row=0, column=0)
  1532. checkbox6Hz = customtkinter.CTkCheckBox(master=frameC, text="6 Hz", command=set_freqvalue6,
  1533. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1535. checkbox6Hz.grid(row=0, column=1)
  1537. checkbox10Hz = customtkinter.CTkCheckBox(master=frameC, text="10 Hz", command=set_freqvalue10,
  1538. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1540. checkbox10Hz.grid(row=0, column=2)
  1542. checkbox20Hz = customtkinter.CTkCheckBox(master=frameC, text="20 Hz", command=set_freqvalue20,
  1543. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1550. checkbox20Hz.grid(row=0, column=3)
  1552. checkbox50Hz = customtkinter.CTkCheckBox(master=frameC, text="50 Hz", command=set_freqvalue50,
  1553. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1555. checkbox50Hz.grid(row=0, column=4)
  1557. checkbox100Hz = customtkinter.CTkCheckBox(master=frameC, text="100 Hz", command=set_freqvalue100,
  1558. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1560. checkbox100Hz.grid(row=0, column=5)
  1562. checkbox1pulse = customtkinter.CTkCheckBox(master=frameC, text="sing. pulse", command=set_stimvalue1,
  1563. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1565. checkbox1pulse.grid(row=1, column=0)
  1566. checkbox5pulse = customtkinter.CTkCheckBox(master=frameC, text="5 pulses", command=set_stimvalue5,
  1567. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1569. checkbox5pulse.grid(row=1, column=1)
  1571. checkbox10pulse = customtkinter.CTkCheckBox(master=frameC, text="10 pulses", command=set_stimvalue10,
  1572. onvalue="on", offvalue="off", font=("Arial Rounded MT Bold", 15), checkbox_width=10, checkbox_height=10, corner_radius=30)
  1574. checkbox10pulse.grid(row=1, column=2)
  1579. frame = customtkinter.CTkFrame(master=root, width=600, fg_color="transparent")
  1580. frame.place(x=645, y=740)
  1581. button = customtkinter.CTkButton(master=frame, width=600, height=70, text="START EVOKED ACTIVITY ANALYSIS",font=("Arial Rounded MT Bold", 25), command=pacemakers,corner_radius=30)
  1582. button.pack(pady=2, padx=1)
  1584. frame = customtkinter.CTkFrame(master=root, fg_color='transparent')
  1585. frame.place(x=10, y=820)
  1586. label= customtkinter.CTkLabel(master=frame, width=1870, text="Selected Channels", font=("Arial Rounded MT Bold", 25), corner_radius=15, text_color='white')
  1587. label.pack(pady=2, padx=1)
  1588. ids= customtkinter.CTkEntry(master=frame, width=1870, height=50, placeholder_text=ROIstr, font=("Arial Rounded MT Bold", 15), corner_radius=15)
  1589. ids.pack(pady=2, padx=1)
  1593. img=PhotoImage(file="NeuroPulseLogo.png")
  1594. frame = customtkinter.CTkFrame(master=root, width=120, height=120, fg_color="transparent")
  1595. frame.place(x=1300, y=40)
  1596. label= customtkinter.CTkLabel(master=frame, width=120, height=120,text="", image=img)
  1597. label.pack(pady=2, padx=1)
  1599. plt.imshow(map_mea, cmap='brg', interpolation='nearest')
  1600. plt.savefig('img.png', bbox_inches='tight', transparent=True, dpi=60)
  1601. img_map=PhotoImage(file="img.png")
  1602. frame = customtkinter.CTkFrame(master=root, width=120, height=120, fg_color="transparent")
  1603. frame.place(x=1040, y=0)
  1604. label= customtkinter.CTkLabel(master=frame, width=120, height=120, text="", image=img_map)
  1605. label.pack(pady=2, padx=1)
  1610. ##spont activity
  1612. frame = customtkinter.CTkFrame(master=root, fg_color='transparent')
  1613. frame.place(x=1280, y=280)
  1614. label= customtkinter.CTkLabel(master=frame, width=600, text="Autorhythmic Cells Spont. Activity Analysis", font=("Arial Rounded MT Bold", 25), text_color='white')
  1615. label.pack(pady=2, padx=1)
  1617. savepacepath.pack(pady=2, padx=1)
  1621. label52= customtkinter.CTkLabel(master=frame, width=600, text="bin size (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1622. label52.pack(pady=2, padx=1)
  1623. VALUE52= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1624. VALUE52.pack(pady=2, padx=1)
  1625. binslider2= customtkinter.CTkSlider(master=frame, width=600, from_=1, to=50, number_of_steps=49, command=binvalue2)
  1626. binslider2.pack(pady=2, padx=1)
  1627. label53= customtkinter.CTkLabel(master=frame, width=600, text="ISI bin limit (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1628. label53.pack(pady=2, padx=1)
  1629. VALUE53= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1630. VALUE53.pack(pady=2, padx=1)
  1631. total_time_slider= customtkinter.CTkSlider(master=frame, width=600, from_=100, to=3000, number_of_steps=29, command=bin_max_value)
  1632. total_time_slider.pack(pady=2, padx=1)
  1633. label54= customtkinter.CTkLabel(master=frame, width=600, text="total time (s)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1634. label54.pack(pady=2, padx=1)
  1635. VALUE54= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1636. VALUE54.pack(pady=2, padx=1)
  1637. total_time_slider2= customtkinter.CTkSlider(master=frame, width=600, from_=0, to=1000, number_of_steps=100, command=total_time)
  1638. total_time_slider2.pack(pady=2, padx=1)
  1648. # label_tw= customtkinter.CTkLabel(master=frame, width=600, text="total time window (ms)", font=("Arial Rounded MT Bold", 15), text_color='white')
  1649. # label_tw.pack(pady=2, padx=1)
  1650. # VALUE_tw= customtkinter.CTkEntry(master=frame, width=600, placeholder_text="", font=("Arial Rounded MT Bold", 15),corner_radius=15)
  1651. # VALUE_tw.pack(pady=2, padx=1)
  1652. # twslider= customtkinter.CTkSlider(master=frame, width=600, from_=500, to=5000, number_of_steps=9, command=twvalue)
  1653. # twslider.pack(pady=2, padx=1)
  1664. frame = customtkinter.CTkFrame(master=root, width=600, fg_color="transparent")
  1665. frame.place(x=1280, y=740)
  1666. button = customtkinter.CTkButton(master=frame, width=600, height=70, text="START SPONT. ACTIVITY ANALYSIS",font=("Arial Rounded MT Bold", 25), command=spont_activity,corner_radius=30)
  1667. button.pack(pady=2, padx=1)
  1669. frame0 = customtkinter.CTkFrame(master=root, fg_color="transparent")
  1671. folderico = PhotoImage(file="folderico.png")
  1672. frame0.place(x=935, y=30)
  1673. buttonBrfile = customtkinter.CTkButton(master=frame0, width=25, text="", command=browse_BRW ,corner_radius=15, image=folderico, fg_color="transparent")
  1674. buttonBrfile.pack(pady=0, padx=0)
  1676. frame0.place(x=935, y=30)
  1677. buttonBrfile = customtkinter.CTkButton(master=frame0, width=25, text="", command=browse_BXR ,corner_radius=15, image=folderico, fg_color="transparent")
  1678. buttonBrfile.pack(pady=0, padx=0)
  1681. frame0 = customtkinter.CTkFrame(master=root, fg_color="transparent")
  1682. frame0.place(x=935, y=128)
  1683. buttonBrfile = customtkinter.CTkButton(master=frame0, width=25, text="", command=browse_ROI ,corner_radius=15, image=folderico, fg_color="transparent")
  1684. buttonBrfile.pack(pady=0, padx=0)
  1686. frame0 = customtkinter.CTkFrame(master=root, fg_color="transparent")
  1687. frame0.place(x=935, y=95)
  1688. buttonBrfile = customtkinter.CTkButton(master=frame0, width=25, text="", command=browse_save ,corner_radius=15, image=folderico, fg_color="transparent")
  1689. buttonBrfile.pack(pady=0, padx=0)
  1692. root.protocol("WM_DELETE_WINDOW", on_closing)
  1693. root.mainloop()

HD-MEA NEUROPulse.py, no license · at the source

Overview

Authors: Eleonora Pali1, Giorgia Pellavio1, Maria Conforti1, Arvin A. Sarkissian2,3, Berna Aliya2,3, Giacomo Sciacca4, Supriya S. Wariyar2,3, Francesco Mainardi4, Mariateresa Tedesco4, Ivan Verduci4, Gendenver Cadiao4, Chiara Cervetto5,6, Jimena Andersen2,3, Fikri Birey2,3, Alessandro Maccione4, Egidio D’Angelo1,7, Lisa Mapelli1
  1. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
  2. Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
  3. Emory Brain Organoid Hub, Atlanta, GA, USA
  4. Brain AG, Pfäffikon, Switzerland
  5. Department of Pharmacology (DIFAR), University of Genoa, Genoa, Italy
  6. Interuniversity Center for the Promotion of the 3Rs Principles in Teaching and Research (Centro 3R), Italy
  7. Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
Institutions: University of Pavia (Italy); Emory University (United States); 3Brain (Switzerland) (Switzerland); University of Genoa (Italy)
Journal: Bio-protocol, volume 16, issue 11, article e5708
Dates: received 13 February 2026; accepted 29 April 2026; published online 5 June 2026
Type: Methods article · Language: English
License: CC BY-NC
Identifiers: DOI 10.21769/bioprotoc.5708 · PMID 42305135 · PMCID PMC13266469 · OpenAlex W7161173022
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: 3D high-density multielectrode array, Acute slices, Brain spheroids, Neural organoids, Electrophysiological recordings, Electrophysiological data analysis, Acute electrophysiological measurements
Journal subjects: Biology, Clinical Protocols
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Animal and human stem cell–derived three-dimensional models to study physio-pathological brain functioning are becoming a gold standard for in vitro electrophysiology, as they enable the recapitulation of complex network properties by accounting for spatial architectural features that better reflect in vivo conditions than simpler 2D models. Standard planar multielectrode arrays (MEAs), typically providing tens of recording electrodes, are commonly used to record activity from 2D neuronal cultures. However, when adapted for use with 3D models, planar 2D MEAs showed limited effectiveness. The main issues are limited specimen adhesion to the chip, a low number of sensing elements, inability to retrieve signals from within the tissue, and reduced perfusion and vitality of the tissue in contact with sensors. To overcome these limitations, a new generation of microchip-based 3D high-density MEAs (3D HD-MEA) has been developed and validated in recent years. This technological advancement has improved the sensing capabilities and the vitality of 3D models, providing a tool tailored to maximize their potential. Here, we present an optimized protocol for neural network activity recordings in 3D models (including acute slices, brain spheroids, and organoids) from various brain regions using 3D HD-MEAs. First, we summarize the critical steps for 1) obtaining viable acute slices from the mouse cerebellum, cortico-hippocampal circuit, and prefrontal cortex, 2) establishing efficient coupling of the slices with the chip, and 3) performing recordings and analyses. We then describe the main procedures required to obtain human and animal brain spheroids and neural organoids, as well as standardized routines to perform effective recordings and analyses. For each section, we highlight the crucial steps, identify tips for specific applications, and propose troubleshooting procedures. For example, the same type of preparation (e.g., acute slices) requires different adjustments when working with different brain areas. The specific information provided here is intended to assist researchers in their daily efforts to obtain efficient and reproducible functional recordings from 3D models by using the cutting-edge technique of 3D HD-MEA.

Key features

• Comprehensive all-in-one guide covering the complete workflow for acquiring electrophysiological data from brain slices, neural region-specific organoids, and brain spheroids.

• Intuitive, step-by-step protocol for brain slice preparation, enriched with practical tips and expert recommendations to ensure high-quality tissue viability.

• Detailed instructions for optimal use of 3D HD-MEA technology, including proper handling of the sample holder for recordings from brain slices, neural organoids, and spheroids.

• In-depth guidance on BrainWave6 software, providing clear procedures for data acquisition, signal detection, and advanced electrophysiological analysis across all sample types.

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

Repository

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

Zenodo 13908319

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 16 files, 1 script
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: h5py (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source:

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;
  • 1 script, 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.

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, issue, pages, dates, 17 authors, 7 keywords, 38 references.

Cite

This paper

Pali, E., Pellavio, G., Conforti, M., Sarkissian, A. A., Aliya, B., Sciacca, G., Wariyar, S. S., Mainardi, F., Tedesco, M., Verduci, I., Cadiao, G., Cervetto, C., Andersen, J., Birey, F., Maccione, A., D’Angelo, E., & Mapelli, L. (2026). Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays. Bio-protocol, 16(11), e5708. https://doi.org/10.21769/bioprotoc.5708

BibTeX

@article{pali2026measuring,
author = {Pali, Eleonora and Pellavio, Giorgia and Conforti, Maria and Sarkissian, Arvin A. and Aliya, Berna and Sciacca, Giacomo and Wariyar, Supriya S. and Mainardi, Francesco and Tedesco, Mariateresa and Verduci, Ivan and Cadiao, Gendenver and Cervetto, Chiara and Andersen, Jimena and Birey, Fikri and Maccione, Alessandro and D’Angelo, Egidio and Mapelli, Lisa},
title = {{Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays}},
journal = {Bio-protocol},
year = {2026},
month = jun,
volume = {16},
number = {11},
pages = {e5708},
publisher = {Bio-protocol, LLC},
issn = {2331-8325},
doi = {10.21769/bioprotoc.5708},
url = {https://doi.org/10.21769/bioprotoc.5708},
pmid = {42305135},
pmcid = {PMC13266469}
}

RIS

TY - JOUR
AU - Pali, Eleonora
AU - Pellavio, Giorgia
AU - Conforti, Maria
AU - Sarkissian, Arvin A.
AU - Aliya, Berna
AU - Sciacca, Giacomo
AU - Wariyar, Supriya S.
AU - Mainardi, Francesco
AU - Tedesco, Mariateresa
AU - Verduci, Ivan
AU - Cadiao, Gendenver
AU - Cervetto, Chiara
AU - Andersen, Jimena
AU - Birey, Fikri
AU - Maccione, Alessandro
AU - D’Angelo, Egidio
AU - Mapelli, Lisa
TI - Measuring Electrophysiological Activity in Acute Brain Slices, Spheroids, and Organoids Using 3D High-Density Multielectrode Arrays
T2 - Bio-protocol
J2 - Bio Protoc
PY - 2026
DA - 2026/06/05
VL - 16
IS - 11
SP - e5708
SN - 2331-8325
PB - Bio-protocol, LLC
DO - 10.21769/bioprotoc.5708
UR - https://doi.org/10.21769/bioprotoc.5708
LA - en
ER -

CSL-JSON

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"family": "Pali",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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