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Conserved role of primary motor cortex in the control of prehension in mice and macaques.

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Python · 335 lines · 12 KB · CC-BY-4.0

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Matched-data plotting and trace-visualization script.
  6. This script:
  7. 1. Builds per-neuron trace data from trial traces.
  8. 2. Plots example mean traces with variance.
  9. 3. Plots session-level mean-trace heatmaps.
  10. 4. Plots FOV overlays with trajectories.
  11. 5. Builds session-by-neuron trace panels.
  12. 6. Plots coefficient of variation and MAD for selected traces.
  14. Expected workspace objects:
  15. - mice_traces
  16. - trial_type_traces
  17. - coordinates
  18. - pnrs
  19. - dates
  20. - xmins / xmaxs (or the script computes limits where needed)
  21. """
  23. import numpy as np
  24. import pandas as pd
  25. import seaborn as sb
  26. import matplotlib
  27. matplotlib.use("TkAgg")
  29. import matplotlib.pyplot as plt
  30. import matplotlib.ticker as mticker
  31. from matplotlib import gridspec
  33. from reach_analysis_funcs import edit_bounding_lines
  36. # ---------------------------------------------------------------------
  37. # plotting style
  38. # ---------------------------------------------------------------------
  39. sb.set(font="Arial", context="talk", font_scale=0.7, style="white")
  40. sb.set_style("ticks")
  43. # ---------------------------------------------------------------------
  44. # helpers
  45. # ---------------------------------------------------------------------
  46. def build_neuron_data_from_trial_traces(trial_traces_df):
  47. """
  48. Build a neuron -> DataFrame dictionary from a trial_traces dataframe.
  50. Each neuron gets a DataFrame whose columns are trials and whose rows
  51. are timepoints.
  52. """
  53. n_neurons = trial_traces_df.iloc[0].shape[0]
  54. neuron_data = {}
  56. for neuron_idx in range(n_neurons):
  57. traces = []
  58. for session in trial_traces_df.index:
  59. session_trials = trial_traces_df.loc[session]
  60. traces.append(np.asarray([session_trials.iloc[n][neuron_idx] for n in range(session_trials.shape[0])]))
  61. neuron_data[neuron_idx] = pd.DataFrame(np.asarray(traces).T)
  63. return neuron_data
  66. def build_session_neuron_data(trial_traces_df):
  67. """
  68. Build a session -> neuron -> DataFrame dictionary.
  70. This matches the nested structure used for the multi-panel variance plots.
  71. """
  72. session_neuron_data = {}
  74. for session_idx, session in enumerate(trial_traces_df.index):
  75. session_trials = trial_traces_df.loc[session]
  76. n_neurons = session_trials.iloc[0].shape[0]
  77. session_neuron_data[session_idx] = {}
  79. for neuron_idx in range(n_neurons):
  80. traces = []
  81. for trial_idx in range(session_trials.shape[0]):
  82. traces.append(np.asarray(session_trials.iloc[trial_idx][neuron_idx]))
  83. session_neuron_data[session_idx][neuron_idx] = pd.DataFrame(np.asarray(traces).T)
  85. return session_neuron_data
  88. def plot_example_mean_traces_with_variance(neuron_data, neuron_ids, time_xlim=(10, 90), time_label="time (s)"):
  89. """Plot selected neuron traces with variance-style line overlays."""
  90. lens = len(neuron_ids)
  91. fig = plt.figure(figsize=(8, 3 * lens), constrained_layout=False)
  92. spec = fig.add_gridspec(ncols=1, nrows=lens, wspace=0.01, hspace=0.02)
  94. for i, neuron_idx in enumerate(neuron_ids):
  95. ax = fig.add_subplot(spec[i])
  96. df_plot = pd.DataFrame(neuron_data[neuron_idx].stack()).droplevel([1], axis=0).reset_index().melt(
  97. id_vars="index", value_vars=0
  98. )
  99. sb.lineplot(data=df_plot, x="index", y="value", ax=ax)
  100. ax.axvline(x=neuron_data[neuron_idx].shape[0] / 2, color="k", alpha=0.4, linewidth=2)
  101. ax.spines["top"].set_visible(False)
  102. ax.spines["right"].set_visible(False)
  104. if i != lens - 1:
  105. ax.spines["bottom"].set_visible(False)
  106. ax.get_xaxis().set_ticks([])
  107. else:
  108. ax.xaxis.set_major_locator(mticker.MaxNLocator(11))
  109. ticks_loc = ax.get_xticks().tolist()
  110. ax.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
  111. ax.set_xticklabels([int(x / 10) - 5 for x in ticks_loc], size=30)
  112. ax.set_xlabel(time_label, size=40)
  114. ticks_loc = ax.get_yticks().tolist()
  115. ax.yaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
  116. ax.set_yticklabels([x for x in ticks_loc], size=20)
  117. ax.set_ylabel(f"neuron {neuron_idx + 1}", rotation=0, labelpad=60, size=20)
  118. ax.set_xlim(*time_xlim)
  120. if i != 0:
  121. ax.spines["left"].set_visible(False)
  123. plt.show()
  126. def plot_selected_session_heatmaps(trial_mean_traces_sorted, sessions, titles=None):
  127. """Plot heatmaps of selected session mean traces."""
  128. lens = len(sessions)
  129. fig, axes = plt.subplots(nrows=1, ncols=lens, figsize=(4 * lens, 4))
  130. axes = np.atleast_1d(axes)
  132. for i, sess in enumerate(sessions):
  133. t = trial_mean_traces_sorted.loc[sess].mean_traces
  134. axes[i].imshow(
  135. t,
  136. aspect="auto",
  137. vmin=np.percentile(t, 85),
  138. vmax=np.percentile(t, 100),
  139. interpolation="none",
  140. )
  141. axes[i].xaxis.set_major_locator(mticker.MaxNLocator(11))
  142. ticks_loc = axes[i].get_xticks().tolist()
  143. axes[i].xaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
  144. axes[i].set_xticklabels([int((x / 10) - 5) for x in ticks_loc], size=20)
  145. axes[i].set_xlabel("time (s)", size=20)
  146. axes[i].set_title(titles[i] if titles is not None else sess, size=20)
  148. plt.show()
  151. def get_session_limits(session_coords, padding=50):
  152. """Compute a display range from a session's coordinate trajectories."""
  153. min_max_vals = [
  154. [np.nanmin(session_coords[j][:, 0]), np.nanmax(session_coords[j][:, 0])]
  155. for j in range(session_coords.shape[0])
  156. ]
  157. ymins = np.min(min_max_vals) - padding
  158. ymaxs = np.max(min_max_vals) + padding
  159. return ymins, ymaxs
  162. def plot_fov_overlays(coordinates, pnrs, sessions):
  163. """Plot FOV images with red trajectory overlays."""
  164. lens = len(sessions)
  165. fig, axes = plt.subplots(nrows=1, ncols=lens, figsize=(5 * lens, 4), sharey=True)
  166. axes = np.atleast_1d(axes)
  168. for i, sess in enumerate(sessions):
  169. session_coords = coordinates.loc[sess]
  170. ymins, ymaxs = get_session_limits(session_coords, padding=50)
  172. axes[i].imshow(
  173. pnrs.loc[sess].T,
  174. aspect="auto",
  175. vmin=np.percentile(pnrs.loc[sess], 85),
  176. vmax=np.percentile(pnrs.loc[sess][0], 100),
  177. cmap="gray",
  178. )
  179. for j in range(session_coords.shape[0]):
  180. axes[i].plot(session_coords[j][:, 1], session_coords[j][:, 0], linewidth=1, color="r")
  182. axes[i].set_ylim(ymaxs, ymins)
  183. axes[i].set_title(sess, size=20)
  184. axes[i].set_axis_off()
  186. plt.show()
  189. def plot_matched_session_group(coordinates, pnrs, session_groups, group_index, xmins, xmaxs, title):
  190. """Plot a matched session group with trajectories overlaid on FOV images."""
  191. sessions = session_groups[group_index]
  192. lens = len(sessions)
  193. fig, axes = plt.subplots(nrows=1, ncols=lens, figsize=(5 * lens, 4))
  194. axes = np.atleast_1d(axes)
  195. fig.subplots_adjust(left=0.1, right=0.8, top=0.995, bottom=0.02, wspace=0.01, hspace=0.1)
  197. stage_labels = [["early", "mid", "late"], ["early", "late"]]
  199. for i, sess in enumerate(sessions):
  200. axes[i].imshow(
  201. pnrs.loc[sess],
  202. aspect=2,
  203. vmin=np.percentile(pnrs.loc[sess], 80),
  204. vmax=np.percentile(pnrs.loc[sess][0], 100),
  205. cmap="gray",
  206. )
  207. for j in range(coordinates.loc[sess].shape[0]):
  208. axes[i].plot(coordinates.loc[sess][j][:, 0], coordinates.loc[sess][j][:, 1], linewidth=1, color="r")
  210. axes[i].set_xlim(xmins, xmaxs)
  211. axes[i].set_title(stage_labels[group_index][i], size=20)
  212. axes[i].set_axis_off()
  214. plt.suptitle(title, size=20)
  215. plt.show()
  218. def plot_session_neuron_panels(session_neuron_data):
  219. """Plot blocks of session-by-neuron trace panels."""
  220. num = 4
  222. for start_idx in np.arange(0, len(session_neuron_data), num):
  223. fig = plt.figure(figsize=(10, 8))
  224. outer = gridspec.GridSpec(1, 4, wspace=0.3, hspace=0.1)
  226. num_n = num
  227. if (len(session_neuron_data) - start_idx) < num:
  228. num_n = int(len(session_neuron_data) - start_idx)
  230. for j, k in enumerate(range(start_idx, start_idx + num_n)):
  231. inner = gridspec.GridSpecFromSubplotSpec(
  232. len(session_neuron_data[0]),
  233. 1,
  234. subplot_spec=outer[j],
  235. wspace=0.3,
  236. hspace=0.01,
  237. )
  239. for i in range(len(session_neuron_data[0])):
  240. ax = plt.Subplot(fig, inner[i])
  241. df_plot = pd.DataFrame(session_neuron_data[k][i].stack()).droplevel([1], axis=0).reset_index().melt(
  242. id_vars="index",
  243. value_vars=0,
  244. )
  245. sb.lineplot(data=df_plot, x="index", y="value", ax=ax)
  246. ax.axvline(x=session_neuron_data[k][i].shape[0] / 2, color="k", alpha=0.4, linewidth=2)
  247. ax.spines["top"].set_visible(False)
  248. ax.spines["right"].set_visible(False)
  249. ax.spines["left"].set_visible(False)
  250. fig.add_subplot(ax)
  252. if i != len(session_neuron_data[0]) - 1:
  253. ax.spines["bottom"].set_visible(False)
  254. ax.get_xaxis().set_ticks([])
  255. else:
  256. ax.xaxis.set_major_locator(mticker.MaxNLocator(11))
  257. ticks_loc = ax.get_xticks().tolist()
  258. ax.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
  259. ax.set_xticklabels([int(x / 10) - 5 for x in ticks_loc], size=30)
  260. ax.set_xlabel("time (s)", size=40, weight="bold")
  262. ticks_loc = ax.get_yticks().tolist()
  263. ax.yaxis.set_major_locator(mticker.FixedLocator(ticks_loc))
  264. ax.set_yticklabels([x for x in ticks_loc], size=20)
  265. ax.set_ylabel("", rotation=0, labelpad=60, size=20)
  266. ax.set_xlim(10, 90)
  267. ax.get_yaxis().set_ticks([])
  268. ax.get_xaxis().set_ticks([])
  270. plt.show()
  273. def plot_trace_cv_and_mad(session_neuron_data, session_idx, neuron_idx):
  274. """Plot CV and MAD for each trace in a selected session/neuron."""
  275. dats = [n.values for _, n in session_neuron_data[session_idx].items()]
  276. fig = plt.figure(figsize=(8, 4))
  278. for da in dats:
  279. cv = np.std(da, axis=1) / np.mean(da, axis=1)
  280. mad = np.mean(np.abs(da - np.mean(da, axis=1, keepdims=True)), axis=1)
  281. plt.plot(cv, "--")
  282. plt.plot(mad)
  284. plt.show()
  287. # ---------------------------------------------------------------------
  288. # primary analysis objects
  289. # ---------------------------------------------------------------------
  290. mouse = "m63"
  291. m = mouse
  293. trial_traces_df = mice_traces[m]["trial_traces"]
  294. dates = trial_traces_df.index.values
  296. neuron_data = build_neuron_data_from_trial_traces(trial_traces_df)
  297. plot_example_mean_traces_with_variance(neuron_data)
  299. trial_mean_traces_sorted = mice_traces[m]["trial_mean_traces_sorted"]
  300. dates = trial_mean_traces_sorted.index.values
  301. datess = np.asarray(dates)[np.asarray([0, 8, 13])]
  302. plot_selected_session_heatmaps(trial_mean_traces_sorted, datess, titles=["early", "mid", "late"])
  304. plot_fov_overlays(coordinates, pnrs, datess)
  306. dates_fil = {
  307. "m63": ["2021-02-03", "2021-02-05", "2021-02-09", "2021-03-17", "2021-03-19", "2021-03-24", "2021-03-31", "2021-05-04", "2021-05-06"],
  308. "m72": ["2021-03-01", "2021-03-04", "2021-03-10", "2021-05-05", "2021-05-19", "2021-08-12", "2021-08-13", "2021-10-01", "2021-10-07"],
  309. "m105": ["2021-12-08", "2021-12-10", "2022-02-03", "2022-02-07", "2022-02-14", "2022-03-14", "2022-03-21", "2022-04-06", "2022-04-08"],
  310. "m106": ["2022-02-02", "2022-02-03", "2022-02-07", "2022-03-16", "2022-03-21", "2022-04-04"],
  311. }
  313. acc_sessions = [["2021-02-03", "2021-03-12", "2021-04-21"], ["2021-05-04", "2021-05-21"]]
  314. dates = [list(coordinates.loc[d].sort_index().index) for d in acc_sessions]
  316. d = 0
  317. xmins, xmaxs = 0, 100
  318. plot_matched_session_group(
  319. coordinates=coordinates,
  320. pnrs=pnrs,
  321. session_groups=dates,
  322. group_index=d,
  323. xmins=xmins,
  324. xmaxs=xmaxs,
  325. title="learning" if d == 0 else "post stroke",
  326. )
  328. # ---------------------------------------------------------------------
  329. # session-by-neuron plots
  330. # ---------------------------------------------------------------------
  331. session_neuron_data = build_session_neuron_data(mice_traces[m]["trial_traces"])
  332. plot_session_neuron_panels(session_neuron_data)
  334. k = max(session_neuron_data.keys())
  335. plot_trace_cv_and_mad(session_neuron_data, session_idx=k, neuron_idx=0)

activity_plotting.py, under CC-BY-4.0 · at the source

Overview

Authors: Francisco Aparicio1,2,3, Harman Ghuman1,2,3, Preeya Khanna1,4, Sapeeda Barati1,2, Robert Morecraft5, Christoph Kirst6, Karunesh Ganguly1,2,4,7
ORCID iDs: Christoph Kirst, Karunesh Ganguly
  1. Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
  2. San Francisco VA Medical Center, San Francisco, CA 94158, USA
  3. These authors contributed equally
  4. California National Primate Research Center, University of California, Davis, Davis, CA 95616, USA
  5. Laboratory of Neurological Sciences, Division of Biomedical and Translational Sciences, Sanford School of Medicine, The University of South Dakota, Vermillion, SD 57069, USA
  6. Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
  7. Lead contact
Institutions: San Francisco VA Medical Center (United States); University of California, San Francisco (United States); University of California, Davis (United States); University of South Dakota (United States); University of California San Francisco Medical Center (United States)
Journal: Cell reports, volume 45, issue 6, article 117419
Dates: published online 1 June 2026; in print 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.celrep.2026.117419 · PMID 42228567 · PMCID PMC13379984 · OpenAlex W7162999766
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), non-human primate (organism), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Motor cortex, Recovery, Lesion, Dexterity, Prehension, Reaching, Grasping, Cp: Neuroscience
MeSH: Hand Strength*, Motor Cortex*, Animals, Biomechanical Phenomena, Female, Forelimb, Male, Mice, Motor Skills (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: RRD VA (I01 RX001640); NINDS NIH HHS (K99 NS124748, R01 NS117406, DP2 NS142717, R01 NS112424); Health Services Research and Development; NICHD NIH HHS (R01 HD111562); U.S. Department of Veterans Affairs; VHA ORD (1I01RX001640); National Institute of Neurological Disorders and Stroke (R01NS117406); National Institutes of Health (R01HD111562, 1K99NS124748)
Citations: cited by 1 paper (Europe PMC); 75 references in the paper
Research resources: anti-GFP RRID:AB_300798, Mouse: C57BL/6J RRID:IMSR_JAX:000664, MATLAB RRID:SCR_001622, rAAV2Retro-hSyn-GCaMP6f RRID:SCR_002448, Python 3.10 RRID:SCR_008394, CaImAn calcium imaging analysis v1.10 RRID:SCR_021533, nVoke miniscope imaging system RRID:SCR_023028, Keypoint-MoSeq RRID:SCR_025032

Abstract

A central goal in neuroscience is to develop cross-species frameworks that reveal conserved principles of movement control. Recent work has suggested a potential species difference in the role of primary motor cortex (M1) during skilled forelimb tasks: in rodents, extensive practice appears to shift control to subcortical circuits, leading to “disengagement” of M1 from task execution. Importantly, there is no evidence of such disengagement in macaques. We hypothesized that these differences instead reflect task demands, particularly the need for fine (i.e., grasping) versus gross motor control. Strikingly, M1 lesions in macaques produced recovery patterns similar to rodents: gross motor control, including an exclusively gross motor task, recovered rapidly, whereas fine motor control exhibited persistent deficits. Detailed kinematic analyses in both macaques and mice performing a single-pellet reach-to-grasp task (RGT) revealed analogous disruptions in the temporal structure of sub-movements. These findings underscore the importance of task design and the translational value of rodent models for understanding motor control and recovery.

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

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Zenodo 19615415

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Footnotes”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (9 files), NumPy (9 files), pandas (9 files), seaborn (9 files), SciPy (7 files), statsmodels (4 files), OpenCV (2 files), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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9 files
At the source: zenodo.org/records/19615415

faparici0/2026_m1_lesion_manuscript

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: the Zenodo archive record
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead
  • 27 September 2026: the link is dead
At the source: github.com/faparici0/2026_m1_lesion_manuscript/tree/inital

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Version 2, 28 September 2026

  • Publisher: n/a → Cell Press

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 8 keywords, 9 MeSH terms, 8 funders, 75 references, 8 RRIDs.

Cite

This paper

Aparicio, F., Ghuman, H., Khanna, P., Barati, S., Morecraft, R., Kirst, C., & Ganguly, K. (2026). Conserved role of primary motor cortex in the control of prehension in mice and macaques. Cell reports, 45(6), 117419. https://doi.org/10.1016/j.celrep.2026.117419

BibTeX

@article{aparicio2026conserved,
author = {Aparicio, Francisco and Ghuman, Harman and Khanna, Preeya and Barati, Sapeeda and Morecraft, Robert and Kirst, Christoph and Ganguly, Karunesh},
title = {{Conserved role of primary motor cortex in the control of prehension in mice and macaques}},
journal = {Cell reports},
year = {2026},
month = jun,
volume = {45},
number = {6},
pages = {117419},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117419},
url = {https://doi.org/10.1016/j.celrep.2026.117419},
pmid = {42228567},
pmcid = {PMC13379984}
}

RIS

TY - JOUR
AU - Aparicio, Francisco
AU - Ghuman, Harman
AU - Khanna, Preeya
AU - Barati, Sapeeda
AU - Morecraft, Robert
AU - Kirst, Christoph
AU - Ganguly, Karunesh
TI - Conserved role of primary motor cortex in the control of prehension in mice and macaques
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/06/01
VL - 45
IS - 6
SP - 117419
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117419
UR - https://doi.org/10.1016/j.celrep.2026.117419
LA - en
ER -

CSL-JSON

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}
}

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