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The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish.

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The authors' code

Python · 277 lines · 10 KB · CC-BY-4.0

  1. #Visual stimulation code used for the size discrimination assay
  2. #Used in the manuscript: Puvvada M., Hladnik T., Zhang Y., Svara F., Lemmens S.,Jorgensen L. V. G., Briggman K., Arrenberg A., Foerster D., Hammerschmidt M., "The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish"
  3. #Method for developing the size discrimination assay was originally described in Barker and Baier, 2015.
  4. #Code written by Fabian Svara
  6. import math
  7. from datetime import datetime, date
  8. import random
  9. from collections import deque
  10. import json
  12. from psychopy import visual, core, monitors, event, platform_specific
  13. from psychopy.tools.monitorunittools import cm2deg, deg2cm, cm2pix, deg2pix
  16. def norm2pix(norm, win):
  17. x = norm[0] * win.size[0] / 2.0
  18. y = norm[1] * win.size[1] / 2.0
  19. return x, y
  22. def pix2norm(pix, win):
  23. x = 2 * pix[0] / win.size[0]
  24. y = 2 * pix[1] / win.size[1]
  26. return x, y
  29. def cm2norm(cm, monitor, win):
  30. px = (cm2pix(cm[0], monitor), cm2pix(cm[1], monitor))
  31. norm = pix2norm(px, win)
  33. return norm
  36. class MovingDot(object):
  37. def __init__(self, win, frame_time, monitor, initial_pos_cm, speed_deg_s, size_deg):
  38. self.initial_pos_cm = initial_pos_cm
  39. self.initial_pos_deg = tuple(cm2deg(xx, monitor) for xx in initial_pos_cm)
  40. self.speed_deg_s = speed_deg_s
  41. self.speed_deg_f = speed_deg_s * frame_time # deg / frame
  42. self.win = win
  43. self.monitor = monitor
  45. self.size_deg = size_deg
  47. self.shape = visual.Polygon(
  48. win, units='deg', edges=100, radius=size_deg/2, fillColor='black', lineColor='black', pos=self.initial_pos_deg)
  50. def update(self):
  51. self.shape.pos += (self.speed_deg_f, 0)
  53. def draw(self):
  54. self.shape.draw()
  56. def visible(self):
  57. pos_x_px = deg2pix(self.shape.pos[0], self.monitor)
  58. if -self.win.size[0] / 2 < pos_x_px < self.win.size[0] / 2:
  59. return True
  60. else:
  61. return False
  64. class DotTrack(object):
  65. def __init__(self, win, frame_time, monitor, heartbeat_f, aquarium_sz_cm, offset_cm, dot_sizes_deg, dot_speed_deg_s, dot_gap_cm):
  66. self.aquarium_sz_cm = aquarium_sz_cm
  67. self.frame_time = frame_time
  68. self.offset_cm = offset_cm
  69. self.win = win
  70. self.frame_count = 0
  71. self.log = []
  72. self.monitor = monitors.Monitor(monitor)
  73. self.dot_sizes_deg = dot_sizes_deg
  74. self.dot_speed_deg_s = dot_speed_deg_s
  75. self.dot_gap_cm = dot_gap_cm
  76. self.heartbeat_f = heartbeat_f
  78. self.reset()
  79. self.build_box()
  81. def reset(self):
  82. self.add_dots(self.dot_sizes_deg, self.dot_speed_deg_s, self.dot_gap_cm)
  83. self.frame_count = 0
  84. self.log = []
  86. def get_track_vertices(self, units='cm'):
  87. x = self.aquarium_sz_cm[0]
  88. y = self.aquarium_sz_cm[1]
  90. box_vert = [[-x/2, y/2], [x/2, y/2], [x/2, -y/2], [-x/2, -y/2], [-x/2, y/2]]
  91. pos = (0, self.offset_cm)
  93. if units == 'cm':
  94. return pos, box_vert
  95. elif units == 'norm':
  96. box_vert_norm = [cm2norm(xx, self.monitor, self.win) for xx in box_vert]
  97. pos_norm = cm2norm(pos, self.monitor, self.win)
  98. return pos_norm, box_vert_norm
  100. def build_box(self):
  101. pos, box_vert = self.get_track_vertices(units='cm')
  102. self.box = visual.ShapeStim(
  103. self.win, units='cm', vertices=box_vert, closeShape=False, lineWidth=1, pos=pos, lineColor='black')
  105. def draw(self, with_dots=True, inc_frame_count=True):
  108. pos, vert = self.get_track_vertices(units='norm')
  110. # size != 1.0 here so as to not occlude parts of the bounding box
  111. _ = visual.Aperture(self.win, size=1.003, shape=vert, pos=pos)
  112. self.box.draw()
  113. if with_dots:
  114. if self.frame_count % self.dot_gap_frames == 0:
  115. if self.dots:
  116. self.active_dots.appendleft(self.dots.pop())
  117. for cur_d in self.active_dots:
  118. cur_d.update()
  119. if cur_d.visible():
  120. cur_d.draw()
  122. if self.frame_count % self.heartbeat_f == 0:
  123. self.log.append([(self.frame_count, xx.size_deg, xx.shape.pos[0], xx.shape.pos[1]) for xx in self.active_dots])
  125. if inc_frame_count:
  126. self.frame_count += 1
  128. def add_dots(self, dot_sizes_deg, speed_deg_s, dot_gap_cm):
  129. """
  130. We construct the PsychoPy dots (Polygons) once in the beginning, and keep track of a subet of active dots
  131. in this class. This is because updating the positions of the Polygons is very expensive and causes dropped
  132. frames when there are many of them.
  134. The active dots are in a FIFO, when dot_gap_frames frames have elapses, a new dot becomes active and is added to
  135. the FIFO. Dots are removed implicitly due to the maximum length of the FIFO, which is calculated based on the
  136. maximal number of dots that will be visible simultaneously. Only dots in the FIFO have their positions
  137. updated and are drawn.
  138. """
  140. dot_gap_deg = cm2deg(dot_gap_cm, self.monitor)
  141. dot_gap_s = dot_gap_deg / speed_deg_s
  142. self.dot_gap_frames = int(round(dot_gap_s / self.frame_time))
  144. max_visible_cm = deg2cm(max(dot_sizes_deg), self.monitor) * 2 + self.aquarium_sz_cm[0]
  145. max_dot_gaps = max_visible_cm / dot_gap_cm
  146. max_visible_dots = int(math.ceil(max_dot_gaps + 1))
  147. print(f'At most {max_visible_dots} dots will be visible simultaneously.')
  149. init_gap_cm = deg2cm(max(dot_sizes_deg), self.monitor) / 2
  150. init_pos_cm = (-self.aquarium_sz_cm[0] / 2 - init_gap_cm, self.offset_cm)
  152. self.dots = []
  153. self.active_dots = deque([], maxlen=max_visible_dots) ## has appendleft() and pop()
  155. side = 1
  156. side_offset = 0.4
  157. for cur_size_deg in dot_sizes_deg:
  158. cur_pos_cm = (init_pos_cm[0], init_pos_cm[1] + side_offset * side)
  159. cur_dot = MovingDot(self.win, self.frame_time, self.monitor, cur_pos_cm, speed_deg_s, cur_size_deg)
  160. self.dots.append(cur_dot)
  161. side *= -1
  163. print(f'Dots in total: {len(self.dots)}')
  166. def write_log(out_fname, tracks, aquarium_sz_cm, aquarium_spacing_cm, top_track_offset_cm, dot_sizes_deg, dot_speed_deg_s,
  167. presentations, dot_gap_cm, heartbeat_s, random_seed):
  168. log_dict = {
  169. 'aquarium_size_cm': aquarium_sz_cm,
  170. 'aquarium_spacing_cm': aquarium_spacing_cm,
  171. 'top_track_offset_cm': top_track_offset_cm,
  172. 'dot_sizes_deg': dot_sizes_deg,
  173. 'dot_speed_deg_s': dot_speed_deg_s,
  174. 'presentations': presentations,
  175. 'dot_gap_cm': dot_gap_cm,
  176. 'heartbeat_s': heartbeat_s,
  177. 'random_seed': random_seed,
  178. }
  180. for idx, cur_t in enumerate(tracks, start=1):
  181. log_dict[f'track_{idx}'] = cur_t.log
  183. with open(out_fname, 'w') as fp:
  184. fp.write(json.dumps(log_dict, indent=4))
  187. def main():
  188. aquarium_sz_cm = (15.6, 2.6)
  189. aquarium_spacing_cm = 2.8
  190. top_track_offset_cm = 4
  192. dot_sizes_deg = [1, 2, 5, 10, 20, 30]
  193. dot_speed_deg_s = 42
  194. presentations = 9
  195. dot_gap_cm = 4
  196. heartbeat_s = 5
  197. show_label_frames = 3
  198. randomize = False
  200. random_seed = 1234
  202. monitor = 'LittleScreen'
  203. win = visual.Window(size=(1024, 768), fullscr=True, screen=1, color=(1, 1, 1), monitor=monitor, checkTiming=True, allowStencil=True)
  204. win.recordFrameIntervals = True
  206. frame_time_s = win.monitorFramePeriod
  207. heartbeat_f = int(round(heartbeat_s / frame_time_s))
  208. print(f'Heartbeat: {heartbeat_f}')
  209. print(f'Monitor has {1 / frame_time_s} Hz refresh rate')
  211. shuffle = random.Random(random_seed).shuffle
  212. dot_sizes = []
  213. for cur_dot_size_deg in dot_sizes_deg:
  214. dot_sizes.extend([cur_dot_size_deg] * presentations)
  215. shuffle(dot_sizes)
  217. tracks = [
  218. DotTrack(win, frame_time_s, monitor, heartbeat_f, aquarium_sz_cm, top_track_offset_cm - aquarium_spacing_cm * 0, dot_sizes, dot_speed_deg_s, dot_gap_cm),
  219. DotTrack(win, frame_time_s, monitor, heartbeat_f, aquarium_sz_cm, top_track_offset_cm - aquarium_spacing_cm * 1, dot_sizes, dot_speed_deg_s, dot_gap_cm),
  220. DotTrack(win, frame_time_s, monitor, heartbeat_f, aquarium_sz_cm, top_track_offset_cm - aquarium_spacing_cm * 2, dot_sizes, dot_speed_deg_s, dot_gap_cm),
  221. DotTrack(win, frame_time_s, monitor, heartbeat_f, aquarium_sz_cm, top_track_offset_cm - aquarium_spacing_cm * 3, dot_sizes, dot_speed_deg_s, dot_gap_cm),
  222. ]
  224. text_sz = 0.25
  226. platform_specific.rush()
  227. while True:
  228. for cur_t in tracks:
  229. cur_t.draw(with_dots=False, inc_frame_count=False)
  230. win.flip()
  232. quit = False
  233. while True:
  234. keys = event.waitKeys()
  235. if 's' in keys:
  236. break
  237. if 'q' in keys:
  238. quit = True
  239. break
  240. if quit:
  241. break
  243. label_frames = 0
  244. now = datetime.now()
  245. date_str = now.strftime('%Y-%m-%dT%H-%M-%S')
  246. label = visual.TextStim(
  247. win, text=date_str,
  248. pos=(aquarium_sz_cm[0] / 2, top_track_offset_cm + aquarium_sz_cm[1] / 2 + text_sz), units='cm',
  249. color=(-1, -1, -1), height=text_sz)
  251. while True:
  252. keys = event.getKeys()
  253. if 'q' in keys:
  254. break
  255. if tracks[0].frame_count % heartbeat_f == 0:
  256. label_frames = show_label_frames
  257. if label_frames:
  258. label.draw()
  259. label_frames -= 1
  260. for cur_track in tracks:
  261. cur_track.draw()
  263. win.flip() # Syncs to monitor frame
  265. write_log(f'experiment-{date_str}.json', tracks, aquarium_sz_cm, aquarium_spacing_cm, top_track_offset_cm,
  266. dot_sizes_deg, dot_speed_deg_s, presentations, dot_gap_cm, heartbeat_s, random_seed)
  268. for cur_t in tracks:
  269. cur_t.reset()
  271. print(f'Overall, {win.nDroppedFrames} frames were dropped.')
  272. win.close()
  273. core.quit()
  276. if __name__ == '__main__':
  277. main()

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

Overview

Authors: Madhuri Puvvada1, Tim Hladnik2, Yue Zhang2, Fabian Svara3, Silke Lemmens1, Louise von Gersdorff Jørgensen4, Kevin Briggman3, Aristides Arrenberg2, Dominique Förster1,5, Matthias Hammerschmidt1,6
ORCID iDs: Madhuri Puvvada
  1. Institute of Zoology, University of Cologne, Cologne, Germany
  2. Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
  3. Max Planck Institute for Neurobiology of Behavior – Caesar, Bonn, Germany
  4. Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
  5. Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  6. Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
Institutions: University of Cologne (Germany); University of Tübingen (Germany); Max Planck Institute for Neurobiology of Behavior – caesar (Germany); University of Copenhagen (Denmark); University Hospital Cologne (Germany)
Journal: iScience, volume 29, issue 6, article 116095
Dates: received 4 July 2025; accepted 7 May 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116095 · PMID 42231971 · PMCID PMC13223957 · OpenAlex W7162116065
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: zebrafish (organism)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: biological sciences
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: German Research Foundation (INST 37/1254-1 FUGG, 233886668); Human Frontier Science Program (RGY0079); National Institute of General Medical Sciences (544966926, GM63904, INST 37/1254-1 FUGG, AR 1076/1-2, 430158665, AR 1076/1-1)
Citations: cited by 1 paper (Europe PMC); 46 references in the paper
Research resources: Goat anti-rabbit Alexa 555 RRID:AB_141784, Goat anti-chicken Alexa 488 RRID:AB_142924, Mouse Anti-SV2 RRID:AB_2315387, Chicken anti-GFP RRID:AB_2534023, Goat anti-mouse Alexa 647 RRID:AB_2535804, Rabbit anti-RFP RRID:AB_591279

Abstract

Energy homeostasis depends on both food intake and behavioral control of energy expenditure. The hypothalamic melanocortin system is classically associated with feeding regulation, but its broader behavioral roles remain less defined. Using larval zebrafish, we show that hypothalamic pro-opiomelanocortin (pomca)-expressing neurons project to the tectum and target neurons expressing the melanocortin receptor Mc4r. Rather than altering prey consumption, melanocortin signaling in this circuit modulates visually guided avoidance of small stimuli, an energy-demanding behavior. Sated larvae, with higher energy reserves, avoid small objects (prey or other, potentially harmful, protozoa) more readily, while hungry larvae do not. Importantly, disrupting Mc4r signaling in sated fish reduces this avoidance behavior, indicating that the melanocortin system gates energy expenditure decisions based on internal state. These findings uncover a previously undescribed function for hypothalamic melanocortin signaling in tuning sensorimotor responses to visual stimuli, not to regulate feeding per se but to modulate behavioral energy allocation.

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

Repository

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

Zenodo 20015261

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PsychoPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.5281/zenodo.20015261

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

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  • 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.

Data and code availability

All data reported in this paper will be shared by the lead contact upon request.

All original code has been deposited at Zenodo and is publicly available at https://doi.org/10.5281/zenodo.20015261 as of the date of publication.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 1 keyword, 3 funders, 45 references, 6 RRIDs.

Cite

This paper

Puvvada, M., Hladnik, T., Zhang, Y., Svara, F., Lemmens, S., von Gersdorff Jørgensen, L., Briggman, K., Arrenberg, A., Förster, D., & Hammerschmidt, M. (2026). The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish. iScience, 29(6), 116095. https://doi.org/10.1016/j.isci.2026.116095

BibTeX

@article{puvvada2026tectal,
author = {Puvvada, Madhuri and Hladnik, Tim and Zhang, Yue and Svara, Fabian and Lemmens, Silke and von Gersdorff Jørgensen, Louise and Briggman, Kevin and Arrenberg, Aristides and Förster, Dominique and Hammerschmidt, Matthias},
title = {{The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116095},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116095},
url = {https://doi.org/10.1016/j.isci.2026.116095},
pmid = {42231971},
pmcid = {PMC13223957}
}

RIS

TY - JOUR
AU - Puvvada, Madhuri
AU - Hladnik, Tim
AU - Zhang, Yue
AU - Svara, Fabian
AU - Lemmens, Silke
AU - von Gersdorff Jørgensen, Louise
AU - Briggman, Kevin
AU - Arrenberg, Aristides
AU - Förster, Dominique
AU - Hammerschmidt, Matthias
TI - The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/22
VL - 29
IS - 6
SP - 116095
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116095
UR - https://doi.org/10.1016/j.isci.2026.116095
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "The tectal melanocortin system modulates energy-dependent visual avoidance behavior in zebrafish",
"container-title": "iScience",
"author": [
{
"family": "Puvvada",
"given": "Madhuri"
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{
"family": "Hladnik",
"given": "Tim"
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{
"family": "Zhang",
"given": "Yue"
},
{
"family": "Svara",
"given": "Fabian"
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{
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{
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"volume": "29",
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"DOI": "10.1016/j.isci.2026.116095",
"PMID": "42231971",
"PMCID": "PMC13223957",
"ISSN": "2589-0042",
"publisher": "Elsevier",
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"language": "en",
"issued": {
"date-parts": [
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22
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}

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