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Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model.

Code ↔ Paper

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

The 3 matches
  1. [1] § 3. Results › 3.2. Extracellular identification of neuronal subtypes in anesthetized finches ↔ ZEB_Classify_Neurons.m, lines 1–73 · score 0.91 · low threshold spiking, awake finches, MSN neurons, low firing rates, waveform features, wide waveforms
  2. [2] § 3. Results › 3.2. Extracellular identification of neuronal subtypes in anesthetized finches ↔ ZEB_Classify_Neurons.m, lines 1–73 · score 0.74 · PAL neurons, high firing rates, Waveform features, wide waveforms, awake, tonic
  3. [3] § 3. Results › 3.2. Extracellular identification of neuronal subtypes in anesthetized finches ↔ ZEB_Classify_Neurons.m, lines 416–442 · score 0.52 · clear bimodality, spike width, trough width, Hz, firing rate, classification

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 453 lines · 20 KB · no license · 3 matches

  1. function [TBL,CLASS_FEATURES] = ZEB_Classify_Neurons(TBL, INFO) %% Determine if there are separate groupings of neurons based on
  2. % waveform features and firing features.
  3. % Depths to consider: Area X is only between 3100 and 3400.
  4. % Add firing rate and other criteria for MSN and PAL neurons from Goldberg
  5. % and Fee 2010 papers (there are 2 papers- on on the typical Areax and one on PAL)
  6. % So 6 cell types that we should distinguish.
  7. %
  8. % There are some high firing wide waveform (look like MSN but rate is too
  9. % high to be MSN neurnos) so we should apply a rate cirterion.
  10. %
  11. % Goldberg and Fee 2010a(striatal) and 2010b(PAL): 6 cell types total in Area X:
  12. % 4 classic (mammalian) and 2 PAL types and both PAL types fire > 60 Hz.
  13. % NOTE: I think going with the 2010a paper is good as it shows that firing
  14. % rate alone separates the PAL from the striatal. They do use the
  15. % complicated IFR and burst detection but in awake finches. Not sure how
  16. % well this applies. NOTE: 2010a did not classify based on bursting but
  17. % used it for post-hoc interpretaion - also used sparsity of firing but
  18. % mostly to assess relationship to song - not relevant for our study.
  19. %
  20. % NOTE: they only used half width - not peak to trough.
  21. % I should perhaps keep to this.
  22. % Also- many of OUR neurons are much larger than theirs.
  24. %
  25. % MSN (type 1): low firing (<6 Hz or even less), WIDE waveform. High Bursting.
  26. %
  27. % FS (type 2) In Area X they fire up to 20Hz DURING SINGING, but much ower (around 8Hz) during rest. Probably more like this during anesthesia.(Fee 2010a).
  28. % NARROW waveform. Low Bursting. I
  29. % think reasonably we can limit the upper range to 60Hz since that's where
  30. % the PAL start (> 60Hz). Probabbly should lower to 50Hz considering
  31. % anesthesia.
  32. %
  33. % TANS (type 3) have a high firing rate but WIDE waveform.
  34. % Can be distinguished from MSNs (even though they have wide
  35. % waveforms) by their high firing rates. Not bursting.
  36. %
  37. % LTS (type 4) low threshold spiking. 1000Hz???? That's what they say - on
  38. % a burst. The paper focuses on these rare neurons. Bursting
  39. %
  40. % PAL1,2 (type HF-1 (GPe-like HF wiht ~300ms pauses), HF-2 (fast tonic)
  41. % from GF2010b second paper). NOTE: this paper does all sorts of fancy PSD and peak rate response analysis to separate the two. Enter at your own risk- all was in waking birds.
  42. % some singing some not.
  43. %
  44. % both subgroups fire >60Hz.
  45. % BUT classification in the paper was done through peak IFR - not mean
  46. % IFR- I don't cluster based on this - but could do it.
  47. % The GF2010 paper did not use waveform to classify cells.
  48. % GF did use IFR smoothed and looked at the peak in this IFR as another
  49. % distinguishing feature to separate HF1 and HF2. HF1 more bursty than HF2.
  50. % in primates - PAL https://pmc.ncbi.nlm.nih.gov/articles/PMC2917366/ have
  51. % 2 classes as well - a high-frequency but that
  52. %
  53. % This is anesthesia however, but since GF did not use waveform, I do not
  54. % see how we can distinguish these cells from others. I can look at the IFR
  55. % and that may be more informative.
  56. %
  57. % UPSHOT: MSNs are relatively easy to ID given low rate and wide waveform.
  58. % PALs are not so clear as only 5 cells fire > 60Hz.
  59. %
  60. % Experimental questions: a-syn, SNCA is the core protein that misfolds and
  61. % forms Lewy Bodies and may drive dopamine cell loss in PD. Zebra Finches
  62. % with human a-syn are predicted to have neuronal alterations homologous to
  63. % predictions in PD. What are those predictions? Interactions between PAL
  64. % and MSN neurons (they should be opposite in a healthy bird).
  65. %
  66. % Look at....
  67. % LtBlue 121, Yellow 160, Red 967, Lavender 162 (sham/non-surgical birds)
  68. % Yellow 160-10/18/22, Red 967-10/13/22, Lavender 162-10/4/22
  69. % TODO: Incorporate firing rate to correct for strange clusters.
  70. % What are the firing rates that we should limit to?
  71. % MSN rate
  72. % TODO: Look at other measures of bursting.
  73. %
  74. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  75. % Cowen 2025
  76. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  77. GP = ZEB_Globals;
  78. PLOT_IT = false;
  79. [clustered_data_dir] = fileparts(which('ZEB_Globals.m'));
  80. %cluster_method = '4 Group'; fig_size = [144 290 1186 343];
  81. % cluster_method = 'Optimal';
  82. % cluster_method = 'Rate_and_width';
  83. cluster_method = 'Rate_and_width_and_PAL';
  85. MSN_max_firing_rate_Hz = 6; % From Fee 2010a - set a cutoff at 10Hz. 6Hz was my original cutoff. THAT SAID, the log frate seems to cutoff at 3Hz.
  86. % MSN_max_firing_rate_Hz = 3; % From Fee 2010a - set a cutoff at 10Hz. 6Hz was my original cutoff. THAT SAID, the log frate seems to cutoff at 3Hz.
  87. PAL_min_firing_rate_Hz = 50; % This is in the literature, but in our data hardly ANY fired at this rate.
  88. % Fast_firing_min_rate_Hz = 20;
  89. Fast_firing_min_rate_Hz = 8; % Maybe +2 and not +4 since 2010aFee indicates that at rest these rates are low.
  90. % peak_trough_thresh_for_wide_ms = 0.35; % nnot using this now as Fee did not use this.
  91. half_width_thresh_for_wide_ms = 0.24; % I had used 0.25. Where is this from? Fee2010a looks like a MUCH lowerr number - like .06!!!
  92. half_width_thresh_for_narrow_ms = 0.22; % I had used 0.25. Where is this from? Fee2010a looks like a MUCH lowerr number - like .06!!!
  93. n_min = 4; % the minimum number of neurons in a category to be considered.
  94. set(0,'DefaultTextInterpreter','none')
  95. clrs = lines(400);
  97. % Store the features used to classify neurons.
  98. CLASS_FEATURES.MSN_max_firing_rate_Hz = MSN_max_firing_rate_Hz;
  99. CLASS_FEATURES.PAL_min_firing_rate_Hz = PAL_min_firing_rate_Hz;
  100. CLASS_FEATURES.Fast_firing_min_rate_Hz = Fast_firing_min_rate_Hz;
  101. CLASS_FEATURES.half_width_thresh_for_wide_ms = half_width_thresh_for_wide_ms;
  102. CLASS_FEATURES.half_width_thresh_for_narrow_ms = half_width_thresh_for_narrow_ms;
  103. CLASS_FEATURES.cluster_method = cluster_method;
  105. % RECUSTER OR LOAD CURRENT CLUSTER?
  106. close all
  108. % 1/median(diff(INFO.WV_x_ms)/1000)
  110. TBL.ACn = TBL.AC - mean(TBL.AC(:,end-20:end),2);
  111. WV_X_IX = INFO.WV_x_ms > -.4 & INFO.WV_x_ms < 1;
  112. WV_X_KM_IX = INFO.WV_x_ms > -.2 & INFO.WV_x_ms < .6;
  113. WVup_X_IX = INFO.WV_x_ms_up > -.4 & INFO.WV_x_ms_up < 1;
  114. AC_X_IX = INFO.AC_x_ms < 50;
  116. % plot(TBL.WaveNorm(:,WV_X_KM_IX)')
  118. [pc,sc] = pca(TBL.WaveNorm(:,WV_X_KM_IX));
  119. TBL.PC1 = sc(:,1);
  120. TBL.PC2 = sc(:,2);
  121. TBL.PC3 = sc(:,3);
  122. TBL.PC4 = sc(:,4);
  126. F = Z_scores([TBL.WaveNorm(:,WV_X_KM_IX) TBL.Half_width_ms TBL.Peak_to_trough_ms]); % My favorite.
  127. % F = Z_scores([TBL.Wave(:,WV_X_KM_IX) TBL.Half_width_ms TBL.Peak_to_trough_ms sc(:,1:3)]); % My favorite.
  128. % F = Z_scores([TBL.Wave(:,WV_X_KM_IX) TBL.Half_width_ms TBL.Peak_to_trough_ms]); %
  129. % F = Z_scores([TBL.Half_width_ms TBL.Peak_to_trough_ms]); %
  130. % F = [TBL.Wave(:,WV_X_KM_IX) TBL.Half_width_ms TBL.Peak_to_trough_ms sc(:,1:3) TBL.AC(:,AC_X_IX)];
  131. % F = [TBL.WaveRaw(:,WV_X_KM_IX) TBL.Peak_to_trough_ms]; % NOPE- think you need to norm WV
  132. % F = [TBL.Wave(:,WV_X_KM_IX) TBL.AC(:,AC_X_IX) TBL.LV TBL.frate];
  133. % F = [TBL.Wave(:,WV_X_KM_IX) TBL.frate];
  134. % [eva, C] = kmeans_optimal_k(F,9);
  135. % IIN THE FUTURE: I would want to further divide the wide waveform up
  136. % into subforms.
  137. [pc,sc] = pca(Z_scores([F TBL.frate]));
  138. TBL.PC1f = sc(:,1);
  139. TBL.PC2f = sc(:,2);
  140. TBL.PC3f = sc(:,3);
  141. TBL.PC4f = sc(:,4);
  144. rng(42) % keep at 42 for now.
  145. C = kmeans(F,2); % do an initially sorting on waveform. Very random.
  146. [cluster_info,C_optimal_clusters] = kmeans_optimal_k(F,6); % do an initially sorting on waveform.
  147. % now find the ones with the narrow waveform (presum IN, HF-1, HF-2) and
  148. % cluster in to 3 groups.
  149. TBL.Neuron_type_NarWide_cat = categorical(NaN(size(C)));
  150. if 0
  151. figure
  152. subplot(1,2,1)
  153. tsne_plot(TBL.WaveNorm(:,WV_X_KM_IX))
  154. subplot(1,2,2)
  155. tsne_plot(F)
  156. end
  158. if 0
  159. % old way
  160. if mean(TBL.Peak_to_trough_ms(C == 1)) > mean(TBL.Peak_to_trough_ms(C == 2))
  161. TBL.Neuron_type_NarWide_cat(C == 1) = 'Wide';
  162. CIX = C==2;
  163. else
  164. TBL.Neuron_type_NarWide_cat(C == 2) = 'Wide';
  165. CIX = C==1;
  166. end
  167. end
  169. % WIX = TBL.Peak_to_trough_ms > peak_trough_thresh_for_wide_ms | TBL.Half_width_ms > half_width_thresh_for_wide_ms; % this breaks it.
  170. WIX = TBL.Half_width_ms > half_width_thresh_for_wide_ms;
  171. NIX = ~WIX & TBL.Half_width_ms < half_width_thresh_for_narrow_ms;
  172. sum(WIX)
  173. sum(NIX)
  174. TBL.Neuron_type_NarWide_cat(1:end) = 'Uncategorized';
  175. TBL.Neuron_type_NarWide_cat(WIX) = 'Wide';
  176. TBL.Neuron_type_NarWide_cat(NIX) = 'Narrow';
  178. TBL.Neuron_type_final_cat = categorical(NaN(size(TBL.Neuron_type_NarWide_cat)));
  179. TBL.Neuron_type_final_cat(1:end) = 'Uncategorized';
  180. if PLOT_IT
  181. CL = C;
  182. CL = C_optimal_clusters;
  183. % CL = TBL.Neuron_type_NarWide_cat;
  184. figure;hist(CL)
  185. u = unique(CL);
  186. clrs = lines(20);
  187. figure
  188. for ii = 1:length(u)
  189. CIX = CL == u(ii);
  190. plot(TBL.WaveNorm(CIX,WV_X_KM_IX)','Color',clrs(ii,:))
  191. hold on
  192. end
  194. figure
  195. subplot(2,2,1)
  196. hist_groups(TBL.Peak_to_trough_ms,CL);xlabel('peak trought width')
  197. subplot(2,2,2)
  198. hist_groups(TBL.Half_width_ms,CL);xlabel('half width')
  199. subplot(2,2,3)
  200. histogram(TBL.Peak_to_trough_ms,50);xlabel('peak trought width')
  201. subplot(2,2,4)
  202. histogram(TBL.Half_width_ms,50);xlabel('half width')
  203. end
  205. switch cluster_method
  206. case '4 Group'
  207. % Further divide the WIDE into 2 groups.
  208. WIX = TBL.Neuron_type_NarWide_cat == 'Wide';
  209. F2 = Z_scores([TBL.WaveNorm(WIX,WV_X_KM_IX) TBL.Wave_max(WIX) sc(WIX,1:4)]);
  210. min_clu = 0;
  211. while min_clu < 20
  212. C = kmeans(F2,2);
  213. c = histcounts(C);
  214. min_clu = min(c);
  215. disp('too few')
  216. end
  217. wix = find(WIX);
  218. WIX1 = false(size(WIX));
  219. WIX1(wix(C==1)) = true;
  220. WIX2 = false(size(WIX));
  221. WIX2(wix(C==2)) = true;
  223. if mean(TBL.Peak_to_trough_ms(WIX1)) > mean(TBL.Peak_to_trough_ms(WIX2))
  224. TBL.Neuron_type_final_cat(WIX1) = 'Wide1';
  225. TBL.Neuron_type_final_cat(WIX2) = 'Wide2';
  226. else
  227. TBL.Neuron_type_final_cat(WIX1) = 'Wide2';
  228. TBL.Neuron_type_final_cat(WIX2) = 'Wide1';
  229. end
  232. % % F2 = Z_scores([ TBL.LV(CIX) TBL.frate(CIX) TBL.Half_width_ms TBL.Peak_to_trough_ms sc(CIX,1:3)]); % My favorite.
  233. NARIX = TBL.Neuron_type_NarWide_cat == 'Narrow';
  234. F2 = Z_scores([ TBL.LV(NARIX) TBL.frate(NARIX) TBL.Half_width_ms(NARIX) TBL.Peak_to_trough_ms(NARIX) sc(NARIX,1:3)]); % My favorite.
  235. min_clu = 0;
  236. while min_clu < 20
  237. C = kmeans(F2,2);
  238. c = histcounts(C);
  239. min_clu = min(c);
  240. disp('too few')
  241. end
  242. C = categorical(C);
  243. C(C=='1') = 'Nar1';
  244. C(C=='2') = 'Nar2';
  245. TBL.Neuron_type_final_cat(NARIX) = categorical(C);
  246. TBL.Neuron_type_final_cat = removecats(TBL.Neuron_type_final_cat);
  247. TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat,{'Wide1' 'Wide2' 'Nar1' 'Nar2'});
  249. case 'Rate_and_width'
  251. SWIX = TBL.Neuron_type_NarWide_cat == 'Wide' & TBL.frate <= MSN_max_firing_rate_Hz;
  252. TBL.Neuron_type_final_cat(SWIX) = 'WideLowRate';
  254. SWIX = TBL.Neuron_type_NarWide_cat == 'Wide' & TBL.frate >= Fast_firing_min_rate_Hz;
  255. TBL.Neuron_type_final_cat(SWIX) = 'WideFastRate';
  256. SWIX = TBL.Neuron_type_NarWide_cat == 'Narrow' & TBL.frate >= Fast_firing_min_rate_Hz;
  257. TBL.Neuron_type_final_cat(SWIX) = 'NarrowFastRate';
  258. SWIX = TBL.Neuron_type_NarWide_cat == 'Narrow' & TBL.frate <= MSN_max_firing_rate_Hz;
  259. TBL.Neuron_type_final_cat(SWIX) = 'NarrowLowRate';
  261. % helps to ensure the MSNs are plotted OVER the others.
  262. TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat, {'Uncategorized','WideLowRate', 'WideFastRate', 'NarrowLowRate', 'NarrowFastRate'});
  263. % TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat, {'Uncategorized','NarrowLowRate', 'NarrowFastRate', 'WideFastRate', 'WideLowRate', 'VeryFastRate'});
  265. figure
  266. gscatter(TBL.Half_width_ms, TBL.Peak_to_trough_ms, TBL.Neuron_type_final_cat);
  267. xlabel('Half_width_ms');ylabel('ms pt width')
  268. axis tight
  269. pubify_figure_axis
  271. figure;
  272. gscatter(log10(TBL.frate), TBL.Peak_to_trough_ms, TBL.Neuron_type_final_cat);
  273. xlabel('log10 Hz');ylabel('ms pt width')
  274. axis tight
  275. pubify_figure_axis
  276. % plot_vert_line_at_zero(log10(MSN_max_firing_rate_Hz))
  277. % plot_vert_line_at_zero(log10(Fast_firing_min_rate_Hz))
  279. if 0
  280. figure
  281. clrs = lines(5);
  282. IX = TBL.Neuron_type_final_cat == 'WideFastRate';
  283. plot(TBL.frate(IX), TBL.Peak_to_trough_ms(IX), 'ko','MarkerFaceColor',clrs(1,:))
  284. hold on
  285. IX = TBL.Neuron_type_final_cat == 'NarrowFastRate';
  286. plot(TBL.frate(IX), TBL.Peak_to_trough_ms(IX), 'ko','MarkerFaceColor',clrs(2,:))
  287. IX = TBL.Neuron_type_final_cat == 'NarrowLowRate';
  288. plot(TBL.frate(IX), TBL.Peak_to_trough_ms(IX), 'ko','MarkerFaceColor',clrs(3,:))
  289. IX = TBL.Neuron_type_final_cat == 'WideLowRate';
  290. plot(TBL.frate(IX), TBL.Peak_to_trough_ms(IX), 'ko','MarkerFaceColor',clrs(4,:))
  291. xlabel('Hz');ylabel('ms pt width')
  292. end
  293. case 'Rate_and_width_and_PAL'
  295. SWIX = TBL.Neuron_type_NarWide_cat == 'Wide' & TBL.frate <= MSN_max_firing_rate_Hz;
  296. TBL.Neuron_type_final_cat(SWIX) = 'WideLowRate';
  297. SWIX = TBL.Neuron_type_NarWide_cat == 'Wide' & TBL.frate >= Fast_firing_min_rate_Hz;
  298. TBL.Neuron_type_final_cat(SWIX) = 'WideFastRate';
  299. SWIX = TBL.Neuron_type_NarWide_cat == 'Narrow' & TBL.frate >= Fast_firing_min_rate_Hz;
  300. TBL.Neuron_type_final_cat(SWIX) = 'NarrowFastRate';
  301. SWIX = TBL.Neuron_type_NarWide_cat == 'Narrow' & TBL.frate <= MSN_max_firing_rate_Hz;
  302. TBL.Neuron_type_final_cat(SWIX) = 'NarrowLowRate';
  303. sum(TBL.Neuron_type_final_cat == 'Uncategorized')
  304. % NOW for the putative PAL cells. I think 50 Hz is reasonable.
  305. % Nothing conclusive has been published for waveform shape, but
  306. % there are putatively 2 types Fee paper).
  307. % NOTE: Only one of these neurons had a LV of >1 and so I got rid
  308. % of the tonic/phasic category.
  309. if 0
  310. SWIX = TBL.frate >= PAL_min_firing_rate_Hz & TBL.LV < 1;
  311. if sum(SWIX) > n_min
  312. TBL.Neuron_type_final_cat(SWIX) = 'VeryFastRateTonic';
  313. end
  314. SWIX = TBL.frate >= PAL_min_firing_rate_Hz & TBL.LV > 1;
  315. disp(['Found ' num2str(sum(SWIX)) ' phasic VHR neurons'])
  316. if sum(SWIX) > n_min
  317. TBL.Neuron_type_final_cat(SWIX) = 'VeryFastRatePhasic';
  318. end
  319. try
  320. TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat, {'Uncategorized', 'WideLowRate','WideFastRate', 'NarrowLowRate', 'NarrowFastRate', 'VeryFastRateTonic', 'VeryFastRatePhasic'});
  321. catch
  322. TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat, {'Uncategorized', 'WideLowRate','WideFastRate', 'NarrowLowRate', 'NarrowFastRate', 'VeryFastRateTonic'});
  323. end
  324. end
  325. SWIX = TBL.frate >= PAL_min_firing_rate_Hz;
  326. TBL.Neuron_type_final_cat(SWIX) = 'VeryFastRate';
  327. TBL.Neuron_type_final_cat = reordercats(TBL.Neuron_type_final_cat, {'Uncategorized', 'WideLowRate','WideFastRate', 'NarrowLowRate', 'NarrowFastRate', 'VeryFastRate'});
  329. figure
  330. subplot(1,3,1)
  331. gscatter(TBL.Half_width_ms, TBL.Peak_to_trough_ms, TBL.Neuron_type_final_cat);
  332. xlabel('Half_width_ms');ylabel('ms pt width')
  333. axis tight
  334. pubify_figure_axis
  336. subplot(1,3,2)
  337. gscatter(TBL.LV, TBL.frate, TBL.Neuron_type_final_cat);
  338. legend off
  339. xlabel('LV');ylabel('frateHz')
  340. axis tight
  341. pubify_figure_axis
  342. % plot_vert_line_at_zero(log10(MSN_max_firing_rate_Hz))
  343. % plot_vert_line_at_zero(log10(Fast_firing_min_rate_Hz))
  345. subplot(1,3,3)
  346. gscatter(log10(TBL.frate), TBL.Peak_to_trough_ms, TBL.Neuron_type_final_cat);
  347. xlabel('log10 Hz');ylabel('ms pt width')
  348. axis tight
  349. pubify_figure_axis
  350. legend off
  352. case 'Optimal'
  353. TBL.Neuron_type_final_cat = categorical(C_optimal_clusters);
  354. end
  355. if 0
  356. %% Rebuttal Figure 1: Firing rate.
  357. % Show this for the pre and post asyn neurons?
  358. GIX = TBL.Group == 'GFP' | TBL.Group == 'ASYN';
  359. % Non-log
  360. figure
  361. subplot(2,1,1)
  362. gscatter(TBL.frate(GIX), TBL.Peak_to_trough_ms(GIX), TBL.Group(GIX), [], 'o^+v');
  363. pubify_figure_axis
  364. if 1
  365. plot_vert_line_at_zero(MSN_max_firing_rate_Hz)
  366. plot_vert_line_at_zero(Fast_firing_min_rate_Hz)
  367. end
  368. set(gca,'xlim',[0 100])
  369. ylabel('Peak to trough width (ms)')
  370. xlabel('Mean firing rate (Hz)')
  372. subplot(2,1,2)
  373. binsize = 2;
  374. edges = 0:binsize:120;
  375. histogram(TBL.frate(GIX),edges,DisplayStyle = 'stairs', LineWidth = 3, EdgeColor = [0 0 0]);
  376. hold on
  377. if 1
  378. plot_vert_line_at_zero(MSN_max_firing_rate_Hz)
  379. plot_vert_line_at_zero(Fast_firing_min_rate_Hz)
  380. end
  381. pubify_figure_axis
  382. set(gca,'xlim',[0 100])
  383. xlabel('Mean firing rate (Hz)')
  384. ylabel('Count')
  385. set(gcf,'Position',[1 1 651 515])
  386. % For reference - also did this in log to ensure that shows nothign...
  387. % Log%%%%%%%%%%%%
  388. nbins = 40;
  389. figure
  390. subplot(2,1,1)
  391. gscatter(log10(TBL.frate(GIX)), TBL.Peak_to_trough_ms(GIX), TBL.Group(GIX), [], 'o^+v', [], [], 1);
  392. pubify_figure_axis
  393. set(gca,'xlim',[0 log10(100)])
  394. ylabel('Peak to trough width (ms)')
  395. if 1
  396. plot_vert_line_at_zero(log10(MSN_max_firing_rate_Hz))
  397. plot_vert_line_at_zero(log10(Fast_firing_min_rate_Hz))
  398. end
  400. % xlabel('Log10(firing rate)')
  402. subplot(2,1,2)
  403. histogram(log10(TBL.frate(GIX)),nbins,DisplayStyle = 'stairs', LineWidth = 3, EdgeColor = [0 0 0]);
  405. pubify_figure_axis
  406. set(gca,'xlim',[0 log10(100)])
  407. xlabel('Log10(firing rate)')
  408. ylabel('Count')
  409. if 1
  410. plot_vert_line_at_zero(log10(MSN_max_firing_rate_Hz))
  411. plot_vert_line_at_zero(log10(Fast_firing_min_rate_Hz))
  412. end
  414. title(sprintf('10^.45 = %1.2f', 10^.45))
  415. set(gcf,'Position',[1 1 651 515])
  416. %% Rebuttal 1.1 Show that my categories have a 'hump' in the separation. They will not.
  417. % part 2 Can the two Wide-Spike Width type [SC2.1]neurons be separated by firing rate with clear bimodal distributions, or is the data more continuous?
  418. IXwlr = TBL.Neuron_type_final_cat == 'WideLowRate'
  419. IXwhr = TBL.Neuron_type_final_cat == 'WideLowRate'
  420. nbins = 40;
  421. figure
  422. subplot(2,1,1)
  423. gscatter(log10(TBL.frate(GIX)), TBL.Peak_to_trough_ms(GIX), TBL.Neuron_type_final_cat(GIX), [], 'o^+v', [], [], 1);
  424. pubify_figure_axis
  425. set(gca,'xlim',[0 log10(100)])
  426. ylabel('Peak to trough width (ms)')
  427. % xlabel('Log10(firing rate)')
  429. subplot(2,1,2)
  430. histogram(log10(TBL.frate(GIX)),nbins,DisplayStyle = 'stairs', LineWidth = 3, EdgeColor = [0 0 0]);
  431. pubify_figure_axis
  432. set(gca,'xlim',[0 log10(100)])
  433. xlabel('Log10(firing rate)')
  434. ylabel('Count')
  435. title(sprintf('10^.45 = %1.2f', 10^.45))
  436. set(gcf,'Position',[1 1 651 515])
  437. if 1
  438. plot_vert_line_at_zero(log10(MSN_max_firing_rate_Hz))
  439. plot_vert_line_at_zero(log10(Fast_firing_min_rate_Hz))
  440. end
  442. end
  445. %%
  446. TBL.Neuron_type_NarWide_cat = removecats(TBL.Neuron_type_NarWide_cat);
  447. TBL.Neuron_type_final_cat = removecats(TBL.Neuron_type_final_cat);
  449. % Neuron_type_IDs = unique(TBL.Neuron_type_NarWide_cat);
  450. % Neuron_type_IDs2 = unique(TBL.Neuron_type_final_cat);
  452. % SAVE THIS STATE AS EVERY TIME YOU RUN THIS YOU WILL GET A NEW RESULT!!!!
  453. % save(fullfile(clustered_data_dir,'Q1_clustered_data'))

ZEB_Classify_Neurons.m, no license · at the source

Overview

Authors: Brian R Dominguez1, Gabriel Holguin2, Madeleine S Daly1, Reed T Bjork1, Stephen L Cowen2,3, Julie E Miller1,4
ORCID iDs: Julie E Miller
  1. Department of Neuroscience, University of Arizona, Tucson, Arizona, United States of America
  2. Department of Psychology, University of Arizona, Tucson, Arizona, United States of America
  3. Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, Arizona, United States of America
  4. Departments of Speech, Language and Hearing Sciences, Neurology, Graduate Interdisciplinary Program in Neuroscience, and the BIO5 Institute, Tucson, Arizona, United States of America
Institutions: University of Arizona (United States); BIO5 Institute
Journal: PloS one, volume 21, issue 7, article e0333158
Dates: received 26 September 2025; accepted 22 June 2026; published online 16 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0333158 · PMID 42461894 · PMCID PMC13374917 · OpenAlex W4414258321
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.11.675693
Status: code verified
Categories: human (organism), other (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: alpha-Synuclein*, Basal Ganglia*, Finches*, Neurons*, Vocalization, Animal*, Animals, Dependovirus, Humans, Male (* major topic)
Topic: Animal Vocal Communication and Behavior (Developmental Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NINDS NIH HHS (R21 NS123512)
Citations: not cited yet (Europe PMC); 111 references in the paper
Research resources: RRID:AB_2192672, RRID:AB_2535813, RRID:SCR_023355

Abstract

Changes in vocal pitch, loudness, and timing are prevalent in Parkinson’s Disease (PD) and a target for early intervention and treatment. The neural mechanisms underlying these impairments are not understood, motivating work in animal models. The adult male zebra finch songbird is uniquely poised for these studies given vocally-dedicated brain nuclei and a quantifiable output (birdsong). Our prior publication revealed that injection of an adeno-associated virus (AAV5) expressing the human (h) alpha-synuclein (hSNCA, a-syn) gene into basal ganglia vocal nucleus Area X results in elevated insoluble a-syn protein and parkinsonian-like changes including softer, shorter, and reduced vocalizations compared to controls. Here, we test the hypothesis that AAV-hSNCA overexpression reduces the firing rate of specific neuronal sub-types in Area X using in vivo recordings in anesthetized finches. Five classes of neurons were differentiated in AAV-treated finches based on waveform width (narrow vs. wide) and firing rates (low vs. high). We found that neurons in the AAV-hSNCA group with wide waveforms exhibited reduced firing rates and enhanced post-peak rebound compared to AAV controls. No differences in firing rate nor waveform shape were detected for the narrow waveform neurons. Our findings provide the first characterization of early a-syn-driven neural activity changes in vocal control neurocircuitry.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

supp:PMC13374917/pone.0333158.s010.zip

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (7)
Size: 8 files, 7 scripts
Software Heritage: not checked
Found in: the supplementary material
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
8 files
At the source: pmc-oa-opendata.s3.amazonaws.com/PMC13374917.1/pone.0333158.s010.zip

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

Tracing map

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

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The raw electrophysiological data, birdsong data, and microscopy images (Fig 2) used in this study will be available at the time of publication through the University of Arizona figshare ReData Repository, under a CC-BY license. https://doi.org/10.25422/azu.data.27868266 All other data including the Matlab code used for the electrophysiological analyses can be found in the Supporting Information.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 MeSH terms, 1 funder, 109 references, 3 RRIDs.

Cite

This paper

Dominguez, B. R., Holguin, G., Daly, M. S., Bjork, R. T., Cowen, S. L., & Miller, J. E. (2026). Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model. PloS one, 21(7), e0333158. https://doi.org/10.1371/journal.pone.0333158

BibTeX

@article{dominguez2026alpha,
author = {Dominguez, Brian R and Holguin, Gabriel and Daly, Madeleine S and Bjork, Reed T and Cowen, Stephen L and Miller, Julie E},
title = {{Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model}},
journal = {PloS one},
year = {2026},
month = jul,
volume = {21},
number = {7},
pages = {e0333158},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0333158},
url = {https://doi.org/10.1371/journal.pone.0333158},
pmid = {42461894},
pmcid = {PMC13374917}
}

RIS

TY - JOUR
AU - Dominguez, Brian R
AU - Holguin, Gabriel
AU - Daly, Madeleine S
AU - Bjork, Reed T
AU - Cowen, Stephen L
AU - Miller, Julie E
TI - Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/07/16
VL - 21
IS - 7
SP - e0333158
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0333158
UR - https://doi.org/10.1371/journal.pone.0333158
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pone.0333158",
"type": "article-journal",
"title": "Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model",
"container-title": "PloS one",
"author": [
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"family": "Dominguez",
"given": "Brian R"
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{
"family": "Daly",
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"family": "Bjork",
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"container-title-short": "PLoS One",
"volume": "21",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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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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