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Effects of electric field direction on TMS-based motor cortex mapping.

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Paper

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The authors' code

Python · 354 lines · 14 KB · no license

  1. """
  2. the 01 step
  3. Random analyses for real subjects
  5. Sequentially permute regression approach over zaps subsamples by adding one zap after another.
  7. This is to be called once per permutation seed (e.g. 1), from slurm.
  9. Reg-factor maps for 10-n zaps are calculated with the sigmoidal approach. This is repeated 100 times by default.
  11. Results are stored in pt/subject/results/congruence_factor/regression_perm/Muscle/_seq_***.hdf5 and .xdmf
  13. Example call
  14. python /data/u_jing_software/git/studies/neuron_regression/Convergence_01_perm.py
  15. --mesh_hdf5 /data/pt_01756/studies/neuron_regression_approach/subjects/09440.22/mesh/charm_4.0.2_refined_M1/m2m_09440.22/09440.22.hdf5
  16. --fn_exp_hdf5 /data/pt_01756/studies/neuron_regression_approach/subjects/09440.22/exp/reg_isi_05/mesh_charm_4.0.2_refined_M1/experiment.hdf5
  17. --fn_e_hdf5 /data/pt_01756/studies/neuron_regression_approach/subjects/09440.22/results/exp_reg_isi_05/electric_field/mesh_charm_4.0.2_refined_M1/roi_midlayer_m1s1pmd/e_scaled.hdf5
  18. --roi_idx midlayer_m1s1pmd
  19. -z 20
  20. -c 7
  21. -n 10
  22. --results_folder /data/pt_01756/studies/neuron_regression_approach/subjects/09440.22/results/congruence_factor/regression_perm/FDI
  23. --verbose True --seed 1 --fun sigmoid4 --refit 10
  26. slurm on mpcdf lets you run 10 jobs in parallel.
  27. #SBATCH --nodes=10
  28. #SBATCH --array=0-10
  29. -> 10 nodes = 1 job started at the same time. 10 times.
  31. #SBATCH --nodes=1
  32. #SBATCH --array=0-100
  33. -> 1 node is one job. is startet 100 times. 10 jobs running maximum.
  35. """
  36. import os
  37. import warnings
  39. import time
  40. import h5py
  41. import pynibs
  42. import argparse
  43. import numpy as np
  44. import pandas as pd
  45. import multiprocessing
  46. from tqdm import tqdm
  47. from functools import partial
  48. from pynibs import write_temporal_xdmf, compute_chunks
  49. from pynibs.neuron.neuron_regression import calc_e_effective
  51. # setup input arguments
  52. parser = argparse.ArgumentParser(description='Run sequential regression permutation for subject/exp/muscle')
  53. # parser.add_argument('-m', '--mep_var', help='mep var type', required=True, type=int)
  55. # files to provide
  56. parser.add_argument('--mesh_hdf5', help='mesh.hdf5 to use for roi conn.', required=True)
  57. parser.add_argument('--fn_exp_hdf5', help='experiment.hdf5 with physlog_data/postproc/EMG', type=str, required=True)
  58. parser.add_argument('--fn_e_hdf5', help='e.hdf5 with /{e_qoi} data', type=str, required=True)
  60. parser.add_argument('--roi_idx', help='Which roi idx.', required=True)
  61. parser.add_argument('--muscle', help='Which roi idx.', default='FDI')
  63. # some default parameters
  64. parser.add_argument('-f', '--fun', help='Which fit function to use. (lin, sigmoid, exp)', default='sigmoid')
  65. parser.add_argument('-q', '--qoi', help='E-qoi (E, E_mag, E_norm, E_tan)', default='E_mag')
  66. parser.add_argument('-z', '--n_zaps', help='N of zaps to draw from', default=400, type=int)
  67. parser.add_argument('-n', '--nth_zaps', help='every nth of zaps to draw from', default=1, type=int)
  68. parser.add_argument('-p', '--n_perms', help='N of permutations', default=100, type=int)
  69. # parser.add_argument('-l', '--layerid', help='Calculation based on neuronal meanfield model on the'
  70. # 'specified layer. ("L23", "L5")', required=False)
  71. parser.add_argument('-m', '--model', help='Calculation based on magnitude, neuronal, or cosine model'
  72. '("mag", "L23", "L5", "cos")', required=False)
  73. parser.add_argument('-c', '--n_cpu', help='N of cpu', default=30, type=int)
  74. parser.add_argument('-r', '--refit', help='number of refits', default=15, type=int)
  75. parser.add_argument('--results_folder', help='results folder', default='', type=str)
  76. parser.add_argument('--startsize', help='start size', default=10, type=int)
  77. parser.add_argument('--verbose', help='Print verbosity information', default=False, type=bool)
  78. parser.add_argument('--seed', help='Seed', default=None, type=int)
  79. parser.add_argument('--stepdown', help='Stepdown approach', default=False, type=bool)
  80. parser.add_argument('--fast_exclude', help='Use linear regression to exclude elms', default=False, type=bool)
  81. parser.add_argument('--sequence_fn', help='Order of zaps.', default=None, type=str)
  83. # get parameters from command line input
  84. args = parser.parse_args()
  86. # select correct worker function
  87. if args.fun.lower().startswith('lin'):
  88. fun = pynibs.linear
  90. elif args.fun.lower().startswith('exp'):
  91. fun = pynibs.exp0
  92. # raise NotImplementedError
  93. elif args.fun.lower().endswith('sigmoid'):
  94. fun = pynibs.sigmoid
  95. elif args.fun.lower().endswith('sigmoid_log'):
  96. fun = pynibs.sigmoid_log
  97. elif args.fun.lower().endswith('sigmoid4'):
  98. fun = pynibs.sigmoid4
  99. elif args.fun.lower().endswith('sigmoid4_log'):
  100. fun = pynibs.sigmoid4_log
  102. elif args.fun.lower().endswith('dummy_fun'):
  103. fun = pynibs.dummy_fun
  104. else:
  105. raise NotImplementedError(f'Unknown function {args.fun}.')
  107. results_folder = args.results_folder
  109. fn_exp_hdf5 = args.fn_exp_hdf5
  110. assert os.path.exists(fn_exp_hdf5)
  111. roi_idx = args.roi_idx
  113. fn_e_hdf5 = args.fn_e_hdf5
  114. assert os.path.exists(fn_e_hdf5)
  116. mesh_fn = args.mesh_hdf5
  117. assert os.path.exists(mesh_fn)
  119. model = args.model
  120. if model == 'L5' or model == 'L23':
  121. geo_hdf5 = f'{mesh_fn[:-26]}roi/midlayer_m1s1pmd/geo_{model}.hdf5'
  122. assert os.path.exists(geo_hdf5)
  123. else:
  124. geo_hdf5 = f'{mesh_fn[:-26]}roi/midlayer_m1s1pmd/geo.hdf5'
  126. seq_order_fn = args.sequence_fn
  127. if seq_order_fn is not None:
  128. assert os.path.exists(seq_order_fn)
  130. print("Starting permutation run with these arguments:")
  131. args_dict = vars(args)
  132. for key in args_dict.keys():
  133. print(f"{key: >15}: {args_dict[key]}")
  134. e_qoi = args.qoi
  135. n_perms = args.n_perms
  136. muscle = args.muscle
  137. seed = args.seed
  138. fast_exclude = args.fast_exclude
  139. stepdown = args.stepdown
  141. # read mep data
  142. mep = pd.read_hdf(fn_exp_hdf5, 'phys_data/postproc/EMG')
  144. # roi connectivity
  145. if model == 'mag' or model == 'cos':
  146. with h5py.File(mesh_fn, 'r') as f:
  147. con = f[f'roi_surface/{roi_idx}/node_number_list'][:]
  148. else:
  149. with h5py.File(mesh_fn, 'r') as f:
  150. con = f[f'roi_surface/{roi_idx}/layers/{model}/node_number_list'][:]
  152. if fun in (pynibs.sigmoid4, pynibs.sigmoid4_log):
  153. # estimate y0 by last .5s of mep data
  154. sampling_rate = len(mep['time'].iloc[0]) / mep['time'].iloc[0][-1]
  155. windows_length_noise_est = .5 # in s
  156. noise_idx = int(sampling_rate * windows_length_noise_est)
  157. assert noise_idx < len(mep['time'].iloc[0])
  159. y0 = np.std(np.vstack([row[noise_idx:] for idx, row in mep[f"mep_filt_data_{muscle}"].items()]))
  160. constants = {'y0': y0}
  161. else:
  162. constants = None
  163. print(f"{'constants': >15}: {constants}")
  165. # extract MEP
  166. mep = mep[f"p2p_{muscle}"]
  168. # extract E
  169. if model == 'mag':
  170. with h5py.File(fn_e_hdf5, 'r') as f:
  171. e = f[e_qoi][:]
  172. elif model == 'cos':
  173. with h5py.File(fn_e_hdf5, 'r') as f:
  174. e = np.abs(f[f"E_norm"][:])
  175. elif model == 'L5' or model == 'L23':
  176. with h5py.File(fn_e_hdf5, 'r') as f:
  177. e_mag = f[f'/{model}/{e_qoi}'][:]
  178. theta = f[f"/{model}/E_theta"][:]
  179. gradient = f[f"/{model}/E_gradient"][:]
  180. e = calc_e_effective(e=e_mag,
  181. layerid=model,
  182. theta=theta,
  183. gradient=gradient,
  184. neuronmodel='sensitivity_weighting',
  185. mep=mep,
  186. waveform='biphasic')
  188. try:
  189. seed = int(os.environ['SLURM_PROCID'])
  190. except:
  191. # print(f"Could not get nodename.")
  192. pass
  193. if seed is not None:
  194. np.random.seed(seed)
  195. perm_idx_list = np.random.randint(0, 1e9, n_perms) # permutate 100 times by default
  197. end_zap = args.n_zaps
  198. nth_zap = args.nth_zaps
  199. n_cpu = args.n_cpu
  200. refit = args.refit
  201. verbose = args.verbose
  202. startsize = args.startsize
  204. # start to itinerant
  205. start = time.time()
  206. pbar = tqdm(total=n_perms - 1)
  207. for perm_idx in perm_idx_list:
  208. if seq_order_fn is None:
  209. res_fn = os.path.join(results_folder, f"allzaps_{e_qoi}_seq_{perm_idx:0>3}.hdf5")
  210. else:
  211. res_fn = os.path.join(results_folder, f"allzaps_{e_qoi}_seq_fixed_order_svd.hdf5")
  212. print(f"{'seed': >15}: {seed}")
  213. print(f"{'perm_idx': >15}: {perm_idx}")
  215. print("-" * 64)
  217. if not os.path.exists(results_folder):
  218. os.makedirs(results_folder)
  220. assert e.shape[0] == mep.shape[0], f"MEP.shape={mep.shape} and E.shape={e.shape} do not fit."
  221. while True:
  222. try:
  223. n_zaps_file, n_elems = e.shape
  224. if end_zap > n_zaps_file:
  225. warnings.warn(f"Only {n_zaps_file} zaps found, {end_zap} requested. Adjusting n_zaps")
  226. end_zap = n_zaps_file - 1
  227. # assert end_zap <= n_zaps_file
  228. # n_elems -= 1
  229. elm_idx_list = np.array(list(range(n_elems)))
  231. if os.path.exists(res_fn):
  232. print(f"{res_fn} exists. Trying to continue from before.")
  233. with h5py.File(res_fn, 'r') as f:
  234. if 'zap_lists' not in f or 'c' not in f:
  235. os.unlink(res_fn)
  236. raise EOFError
  237. zap_lists = []
  238. if end_zap > len(f['/zap_lists'].keys()):
  239. warnings.warn(f"Only {len(f['/zap_lists'].keys())} zap lists found. "
  240. f"Adjusting end_zap={end_zap}.")
  241. end_zap = len(f['/zap_lists'].keys())
  242. for zap_i in f[f'/zap_lists/']:
  243. zap_lists.append(f[f'/zap_lists/'][zap_i][:])
  245. # now remove the zap_lists that already have been computed
  246. to_compute = []
  247. # for key in f['/zap_lists'].keys():
  248. # if str(len(f['/zap_lists'][key][:])) not in f['/c']:
  249. # to_compute.append(int(key))
  250. # to_compute = [x for x in to_compute if x > 9]
  251. try:
  252. c_computed = [int(i) for i in (f['/c'].keys())]
  253. to_compute_zaps = [i for i in range(startsize, end_zap) if i not in c_computed]
  254. org_list = [int(i) for i in (f['/zap_lists'].keys())]
  255. to_compute = np.where(np.isin(np.array(org_list), np.array(to_compute_zaps)))[0] # the indices of the missing zaps
  256. print(f"{len(c_computed)} c-maps found, {len(to_compute)} left to compute: {to_compute_zaps} at the {to_compute} indices.")
  257. except KeyError:
  258. pass
  259. else:
  260. if seq_order_fn is None:
  261. # here we randomly pick efields
  262. zap_list = range(n_zaps_file) # sample from full set
  263. if seed:
  264. np.random.seed(perm_idx)
  265. zap_list = np.random.choice(zap_list, size=end_zap, replace=False).tolist()
  267. zap_lists = []
  268. if nth_zap == 1:
  269. for size_idx in range(len(zap_list)):
  270. zap_lists.append(zap_list[0:size_idx])
  271. to_compute = list(range(startsize, end_zap))
  272. else:
  273. for size_idx in range(10, end_zap + 1, nth_zap):
  274. zap_lists.append(zap_list[0:size_idx])
  275. startsize = 0
  276. to_compute = list(range(startsize, int(end_zap / nth_zap)))
  278. with h5py.File(res_fn, 'a') as f:
  279. f.create_group('zap_lists')
  280. for i in range(len(zap_lists)):
  281. f.create_dataset(f'zap_lists/{i}', data=np.array(zap_lists[i]))
  282. subsample_size = [len(i) for i in zap_lists]
  283. f.create_dataset('sample_size', data=subsample_size)
  284. f.create_dataset('zap_index_lists', data=zap_list)
  286. else:
  287. # choose order from fn
  288. with h5py.File(seq_order_fn, 'r') as seq:
  289. with h5py.File(res_fn, 'a') as f:
  290. zap_lists = []
  291. keys = list(seq['zap_lists'].keys())
  292. keys.sort(key=lambda x: '{0:0>8}'.format(x).lower())
  293. if len(seq['zap_lists']['0'][:]) != 0:
  294. zap_lists.append([])
  296. for key in keys:
  297. zap_lists.append(seq['zap_lists'][key][:])
  299. # add list for N zaps
  300. # zap_lists.append(np.array([i for i in range(startsize, e.shape[0])]))
  301. subsample_size = [len(i) for i in zap_lists]
  302. f.create_dataset('sample_size', data=subsample_size)
  303. for lst in zap_lists:
  304. # print(len(lst))
  305. f.create_dataset(f'zap_lists/{len(lst)}', data=lst)
  306. f.copy(source=seq['zap_index_lists'], dest='zap_index_list')
  307. to_compute = list(range(startsize, end_zap))
  309. break
  310. except Exception:
  311. print("An error occurred while reading files. retrying.")
  312. time.sleep(1)
  314. # create pool here and provide to cmap function.
  315. n_cpu_available = multiprocessing.cpu_count()
  316. n_cpu = min(n_cpu, n_cpu_available)
  318. l = multiprocessing.Lock()
  319. pool = multiprocessing.Pool(n_cpu, initializer=pynibs.init, initargs=(l, zap_lists, res_fn))
  321. workhorse = partial(pynibs.nl_hdf5_single_core_write,
  322. elm_idx_list=elm_idx_list,
  323. e_matrix=e,
  324. mep=mep,
  325. fun=fun,
  326. n_refit=refit,
  327. con=con,
  328. return_fits=False,
  329. constants=constants,
  330. stepdown=stepdown,
  331. verbose=verbose)
  333. pool.map(workhorse, to_compute)
  334. # for zap_list_it in zap_lists:
  335. #
  336. # subsample_size.append(len(zap_list_it))
  337. # zaplist.append(zap_list_it)
  338. pool.close()
  340. # with h5py.File(res_fn, 'a') as f:
  341. # f.create_group('c')
  342. # [f.create_dataset(f'c/{i}', data=c[i]) for i in range(len(c))]
  343. # f.create_dataset('sample_size', data=subsample_size)
  344. # f.create_group('zap_index_lists')
  345. #
  346. # [f.create_dataset(f'zap_index_lists/{i}', data=zaplist[i]) for i in range(len(zaplist))]
  347. write_temporal_xdmf(res_fn, data_folder='c',
  348. hdf5_geo_fn=geo_hdf5, overwrite_xdmf=True)
  350. pbar.update(1)
  352. stop = time.time()
  353. duration = stop - start
  354. print(f'success! Permutation analysis took {duration} seconds.')

Convergence_01_perm.py at commit 78149c6, no license · at the source

Overview

Authors: Ying Jing1,2,3, Ole Numssen1,2,4, Gesa Hartwigsen2,5, Thomas R Knösche1,6, Konstantin Weise1,7
ORCID iDs: Ying Jing
  1. Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  2. Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. Complex Networks and Brain Dynamics Group, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
  4. TMS Group, Department of Neuroscience and Biomedical Engineering, Aalto University, Finland
  5. Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany
  6. Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
  7. Institute of Electrical Energy Technology, Leipzig University of Applied Sciences, HTWK, Leipzig, Germany
Institutions: Czech Academy of Sciences (Czechia); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Czech Academy of Sciences, Institute of Computer Science (Czechia); Aalto University (Finland); Leipzig University (Germany); Technische Universität Ilmenau (Germany); Leipzig University of Applied Sciences (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1211
Dates: received 27 July 2025; accepted 26 March 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1211 · PMID 42027741 · PMCID PMC13100673 · OpenAlex W7143301135
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: transcranial magnetic stimulation, TMS mapping, electrical field modeling, average response model, motor cortex
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: European Research Council (101043747, ERC-2021-COG 101043747); Deutsche Forschungsgemeinschaft (HA 6314/3-1); German Research Foundation (HA 6314/4-2, Ha 6314/9-1); China Scholarship Council; Bundesministerium für Bildung und Forschung (01GQ2201); Federal Ministry of Education Germany
Citations: cited by 3 papers (Europe PMC); 64 references in the paper

Abstract

Transcranial magnetic stimulation (TMS) induces an electric field (E-field) that drives neuronal activation, but the optimal model for predicting cortical responses remains unclear. Traditional TMS motor mapping typically relies on the E-field magnitude or its normal component as a proxy for excitability, overlooking the influence of neuronal morphology and orientation. In this study, we aimed to refine TMS motor mapping by incorporating an average response model that accounts for both E-field magnitude and directional sensitivity. We conducted a regression-based TMS mapping experiment in 14 participants to identify cortical origins of motor-evoked potentials (MEPs) from the first dorsal interosseous (FDI) muscle. Firing thresholds were estimated for excitatory neurons in cortical layers 2/3 and 5, and regression was performed between MEPs and three E-field quantities: the E-field magnitude (magnitude model), the normal component of E-field (cosine model), and an effective E-field that adjusts magnitude by orientation-specific thresholds (neuron model). Models were compared based on regression fit, convergence speed, and functional validation using optimized coil placements tested in 10 additional participants. Results showed that the magnitude and neuron models performed similarly and robustly, whereas the cosine model explained significantly less variance, required more TMS pulses for stable mapping, and produced the weakest MEPs in validation. These findings suggest that while directional sensitivity plays a role, E-field magnitude remains the dominant factor in motor cortex activation.

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

Repository

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

gitlab.gwdg.de/tms-localization/papers/neuron-regression

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State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 78149c6b70bccb4c0ab7247fbdcf86562a7e0d92, 25 July 2025
Languages: Python (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), h5py (4 files), pandas (3 files), Matplotlib (2 files), SciPy (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
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6 files
At the source: gitlab.gwdg.de/tms-localization/papers/neuron-regression.git

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

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Data

Datasets cited

Data and Code Availability

The data are available on the Open Science Framework at: https://osf.io/9f3bc. The code is available on GitLab at: https://gitlab.gwdg.de/tms-localization/papers/neuron-regression.git.

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

Versions

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Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 6 funders, 64 references.

Cite

This paper

Jing, Y., Numssen, O., Hartwigsen, G., Knösche, T. R., & Weise, K. (2026). Effects of electric field direction on TMS-based motor cortex mapping. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1211. https://doi.org/10.1162/imag.a.1211

BibTeX

@article{jing2026effects,
author = {Jing, Ying and Numssen, Ole and Hartwigsen, Gesa and Knösche, Thomas R and Weise, Konstantin},
title = {{Effects of electric field direction on TMS-based motor cortex mapping}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1211},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1211},
url = {https://doi.org/10.1162/imag.a.1211},
pmid = {42027741},
pmcid = {PMC13100673}
}

RIS

TY - JOUR
AU - Jing, Ying
AU - Numssen, Ole
AU - Hartwigsen, Gesa
AU - Knösche, Thomas R
AU - Weise, Konstantin
TI - Effects of electric field direction on TMS-based motor cortex mapping
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/21
VL - 4
SP - IMAG.a.1211
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1211
UR - https://doi.org/10.1162/imag.a.1211
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1211",
"type": "article-journal",
"title": "Effects of electric field direction on TMS-based motor cortex mapping",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Jing",
"given": "Ying"
},
{
"family": "Numssen",
"given": "Ole"
},
{
"family": "Hartwigsen",
"given": "Gesa"
},
{
"family": "Knösche",
"given": "Thomas R"
},
{
"family": "Weise",
"given": "Konstantin"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1211",
"DOI": "10.1162/imag.a.1211",
"PMID": "42027741",
"PMCID": "PMC13100673",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1211",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
21
]
]
}
}

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