OSCR

Environmental Enrichment Preserves Retrosplenial Parvalbumin Density and Cognitive Function in Female 5xFAD Mice.

Code ↔ Paper

4 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 4 matches
  1. [1] § Materials and Methods › Imaging and image analysis › Functional connectivity analyses ↔ Terstege2023A/analyses/wholebrain/WholeBrainAnalysis.m, lines 1–64 · score 0.64 · Allen Mouse Brain, masks, microscope, neuroanatomical, Atlas, Fos
  2. [2] § Materials and Methods › Imaging and image analysis › Functional connectivity analyses ↔ Terstege2022A/WholeBrain/MATLAB/WholeBrainAnalysis_v101.m, lines 1–60 · score 0.63 · Allen Mouse Brain, masks, microscope, neuroanatomical, Atlas, Fos
  3. [3] § Materials and Methods › Statistics and data visualization ↔ Terstege2022A/WholeBrain/MATLAB/WholeBrainAnalysis_v101.m, lines 213–353 · score 0.52 · functional connectivity, graphs, Circle, Pearson, MATLAB, networks
  4. [4] § Results › Enriched housing conditions are sufficient to maintain healthy local activity and global functional connectivity of the RSC in female 5xFAD mice ↔ Terstege2022A/WholeBrain/MATLAB/WholeBrainAnalysis_v101.m, lines 213–353 · score 0.51 · correlation coefficient, functional connectivity, Circle, Pearson, networks, global

Paper

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The authors' code

MATLAB · 442 lines · 17 KB · CC-BY-4.0 · 3 matches

  1. %% WholeBrainAnalysis.m
  2. %
  3. % Whole Brain Analysis
  4. %
  5. % It is recommended that users read through the documentation in full
  6. % prior to using this analysis. This applies to each section in the code.
  7. % Sections are to be ran sequentially.
  8. %
  9. % All outputs can be accessed through the structure element "WB" in the
  10. % MATLAB workspace
  11. %
  12. % *GENERAL INFORMATION*
  13. %
  14. % WholeBrainAnalysis.m was developed for the compilation and analysis of
  15. % datasets collected using the Whole Brain software suite developed by
  16. % Daniel Furth (http://www.wholebrainsoftware.org) and incorporates a
  17. % modification using scripts and plugins
  18. % (https://github.com/dterstege/CavalieriPointMask) developed by
  19. % Dylan Terstege.
  20. %
  21. % Whole Brain is a neuroanatomical information system. Neuroanatomical
  22. % data obtained from microscope images is encoded and stored in
  23. % stereotactic coordinate form within the Allen Mouse Brain Atlas.
  24. % The software suite can be accessed at http://www.wholebrainsoftware.org
  25. %
  26. %
  27. % Additional Whole Brain scripts can be obtained from Dylan Terstege upon
  28. % request.
  29. %
  30. % Can be saved and re-loaded at any time using Processes X and Y, found
  31. % at the end of this script.
  32. %
  33. % NOTE: Avoid using spaces in any file, folder, or variable names. MATLAB
  34. % doesn't handle this well and in many cases the program will throw an
  35. % error in response.
  36. %
  37. % INPUTS:
  38. % "cells.csv" files: naming structure is not of particular
  39. % importance, but these files should contain the number of segmented
  40. % LABELS per region. This is a raw Whole Brain output. In the example
  41. % of c-Fos mapping, this is the number of c-fos+ cells per region.
  42. % "grids.csv" files: naming structure is not of particular
  43. % importance, but these files hsoudl contain the number of segmented
  44. % POINTS per region. This is a raw Whole Brain output. These points
  45. % are counted from the Cavalier Point Masks
  46. %
  47. % Requires "WB_Atlas.mat" to be in the MATLAB path
  48. %
  49. % *DEVELOPER INFORMATION*
  50. %
  51. % Version 1.0.1
  52. %
  53. %
  54. % Created 11/06/2021 Dylan Terstege (https://github.com/dterstege)
  55. % Epp Lab, University of Calgary (https://epplab.com)
  56. % Contact: [email hidden]
  58. welcomemessage = [' *** Running Whole Brain Analysis *** ', newline, ' Version 1.0.1',newline, newline, 'This program was written and developed by Dylan Terstege'];
  59. disp(welcomemessage);
  60. clear welcomemessage
  62. %% 01. Initialization
  63. % During this process, data will be loaded into the MATLAB structure
  64. % element 'WB' for later use. This step in the analysis is reasonable
  65. % hands-on and requires the user to point the script to several
  66. % directories and input animal IDs. The results of this data loading
  67. % can be found under the 'inputs' element within 'WB'
  68. %
  69. % Requires "WB_Atlas.mat" to be in the MATLAB path
  70. %
  71. % There are several variables which the user should adjust prior to
  72. % starting the analysis. These are listed below
  73. %
  74. % Group Information:
  75. % Input number of groups
  76. % default: groupnum = 2
  77. % Input group identifiers
  78. % default: groups = ["CTRL" "MWT"]
  79. % Input number of animals per group
  80. % default: pergroup = [10 10]
  81. % Input individual IDs
  82. % defaults:
  83. % IDs.CTRL = ["DT42br" "DT44br" "DT45w" "DT63" "DT64" "DT65" "DT71" "DT76" "DT92" "DT93"];
  84. % IDs.MWT = ["DT11" "DT12" "DT12bl" "DT12br" "DT13" "DT14" "DT15" "DT16w" "DT17br" "DT23"];
  85. %
  86. % Image Specifications:
  87. % Input area factor
  88. % default: areafactor = 0.56286606
  92. %clear structure to start fresh
  93. clear WB
  95. %input number of groups
  96. WB.info.groupnum = 2;
  97. %input group identifiers
  98. WB.info.groups = ["CTRL" "MWT"]; %manually adjust, no spaces
  99. %input number of animals per group
  100. WB.info.pergroup = [10 10]; %manually adjust
  101. %input individual IDs
  102. %make sure group names appear in same order as inputted in the group
  103. %identifiers variable
  104. WB.info.IDs.CTRL = ["DT42br" "DT44br" "DT45w" "DT63" "DT64" "DT65" "DT71" "DT76" "DT92" "DT93"];
  105. WB.info.IDs.MWT = ["DT11" "DT12" "DT12bl" "DT12br" "DT13" "DT14" "DT15" "DT16w" "DT17br" "DT23"];
  107. %area factor
  108. WB.info.areafactor = 0.56286606; % ((dist/pix)*(pix/point))^2
  109. %load full whole brain atlas
  110. WB.info.fullatlas = load('WB_Atlas.mat');
  112. %load whole brain output files
  113. for ii=1:WB.info.groupnum(1)
  114. for iii=1:WB.info.pergroup(ii)
  115. %import raw data
  116. %import cells
  117. prompt=strcat("Select parent folder for mouse ",(WB.info.IDs.(WB.info.groups(ii))(iii))," in ",WB.info.groups(ii)," group:");
  118. disp(prompt);
  119. parentpath=uigetdir; %identify parent directory
  120. cellpath=fullfile(parentpath,'cell');
  121. celldir=dir(cellpath); %set directory
  122. cellcsv={celldir(:).name}; %scan directory
  123. cellcsv(ismember(cellcsv,{'.','..'}))=[]; %filter
  124. cellcsv=cellcsv(~startsWith(cellcsv,'._')); %removes dot underscore files
  125. csvindex=contains(cellcsv,'.csv'); %identify csv
  126. cellcsv=cellcsv(csvindex); %list csv files
  127. cellcount=numel(cellcsv); %count csv
  128. WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).cells=NaN(numel(WB.info.fullatlas.WB_Atlas),cellcount);
  129. for iiii=1:cellcount
  130. data=readcell(char(fullfile(cellpath,cellcsv(iiii)))); %load cell file
  131. regions=data(2:end,2); %identify regions in file
  132. counts=data(2:end,3);
  133. [~,k]=intersect(WB.info.fullatlas.WB_Atlas,regions); %find index of regions in file with full atlas
  134. WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).cells(k,iiii)=cell2mat(counts);
  135. end
  137. %import grids
  138. gridpath=fullfile(parentpath,'grid');
  139. griddir=dir(gridpath); %set directory
  140. gridcsv={griddir(:).name}; %scan directory
  141. gridcsv(ismember(gridcsv,{'.','..'}))=[]; %filter
  142. gridcsv=gridcsv(~startsWith(gridcsv,'._')); %removes dot underscore files
  143. csvindex=contains(gridcsv,'.csv'); %identify csv
  144. gridcsv=gridcsv(csvindex); %list csv files
  145. gridcount=numel(gridcsv); %count csv
  146. WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).areas=NaN(numel(WB.info.fullatlas.WB_Atlas),gridcount);
  147. for iiii=1:gridcount
  148. data=readcell(char(fullfile(gridpath,gridcsv(iiii)))); %load grid file
  149. regions=data(2:end,2); %identify regions in file
  150. area=cell2mat(data(2:end,3));
  151. area=area*(WB.info.areafactor);
  152. [~,k]=intersect(WB.info.fullatlas.WB_Atlas,regions); %find index of regions in file with full atlas
  153. WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).areas(k,iiii)=area;
  154. end
  155. end
  156. end
  157. %process successful
  159. %clean up variable space
  160. clearvars -except WB
  162. %process complete
  163. disp('Process 1. Initialization Complete');
  165. %% 02. Regional Label Density
  166. % During this process, the density of the segmented label will be
  167. % calculated using a user-defined atlas organization. This process is
  168. % completely hands-off once the atlas has been created and added to the
  169. % MATLAB path. Its name should be inputed to the script in place of the
  170. % default atlas.
  171. %
  172. % Requires "User_Atlas_98reg.mat", or whatever the user names it, to be in the
  173. % MATLAB path
  174. %
  175. % User must have the WB structure in their workspace prior to starting
  176. % this process
  178. %load user atlas
  179. WB.info.useratlas = load('User_Atlas_98reg.mat'); %adjust as needed
  180. regnum=((numel(WB.info.useratlas.User_Atlas_98reg))/3)-1; %number of regions %adjust as needed
  181. regiondims=cell2mat(WB.info.useratlas.User_Atlas_98reg(2:end,2:3)); %identifies where regions start and end %adjust as needed
  183. for ii=1:WB.info.groupnum(1)
  184. %create output arrays
  185. WB.outputs.(WB.info.groups(ii)).totalcells=NaN(regnum,WB.info.pergroup(ii)); %NaN array
  186. WB.outputs.(WB.info.groups(ii)).totalarea=NaN(regnum,WB.info.pergroup(ii)); %NaN array
  187. WB.outputs.(WB.info.groups(ii)).density=NaN(regnum,WB.info.pergroup(ii)); %NaN array
  188. for iii=1:WB.info.pergroup(ii)
  189. cells=nansum(WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).cells,2); %total number of segmented labels in each region
  190. areas=nansum(WB.inputs.(WB.info.groups(ii)).(char(WB.info.IDs.(WB.info.groups(ii))(iii))).areas,2); %total area of each region
  191. for iiii=1:regnum %sequentially run through regions in custom atlas organization
  192. temp=regiondims(iiii,:); %isolate dimensions of region
  193. tempcell=sum(cells(temp(1):temp(2),1)); %isolate and sum row(s) within region bounds
  194. temparea=sum(areas(temp(1):temp(2),1)); %isolate and sum row(s) within region bounds
  195. tempdensity=tempcell/temparea; %calculate density
  196. WB.outputs.(WB.info.groups(ii)).totalcells(iiii,iii)=tempcell; %save cells
  197. WB.outputs.(WB.info.groups(ii)).totalarea(iiii,iii)=temparea; %save area
  198. WB.outputs.(WB.info.groups(ii)).density(iiii,iii)=tempdensity; %save density
  199. end
  200. %corrections
  201. idx=((WB.outputs.(WB.info.groups(ii)).totalarea)==0);
  202. WB.outputs.(WB.info.groups(ii)).totalcells(idx)=0;
  203. WB.outputs.(WB.info.groups(ii)).density(idx)=nan;
  204. end
  205. end
  207. %clean up variable space
  208. clearvars -except WB
  210. %process complete
  211. disp('Process 2. Regional Label Density Complete');
  213. %% 03. Basic Network Analyses
  214. % During this process, pairwise correlation matrices will be generated
  215. % from the segmented label densities from
  216. %
  217. % Required packages in MATLAB path:
  218. % fdr_bh.m (https://www.mathworks.com/matlabcentral/fileexchange/27418-fdr_bh)
  219. % degrees_und.m (https://sites.google.com/site/bctnet/)
  220. %
  221. % Outputs:
  222. % - Correlation Matrices
  223. % Pearson's R
  224. % p-value matrix
  225. % - Binarized Adjacency Matrix
  226. % - Survival curve of functional connections with increased Pearson's
  227. % threshold
  228. %
  229. % User must have the WB structure in their workspace prior to starting
  230. % this process
  231. %
  232. % NOTE: 'NaN' values in density matrix will have major implications on the
  233. % correlation matrices. Be sure to consider these values very carefully
  234. % during interpretation of results. NaN occurs when a region was not
  235. % registered for an animal
  237. %user input variables
  238. %thresholding
  239. % significance threshold for pariwise correlations
  240. WB.info.networks.threshold.pthresh = 0.0005;
  241. % false discovery rate threshold for pariwise correlations
  242. WB.info.networks.threshold.fdrthresh = 0.05;
  243. % cutoff for binarizing matrix
  244. WB.info.networks.threshold.binary = 0.95;
  245. %correlation strength survival
  246. % minimum value to look at
  247. WB.info.networks.corrstrength.startR=0.00;
  248. % maximum value to look at
  249. WB.info.networks.corrstrength.lastR=1.00;
  250. % resolution of analysis
  251. WB.info.networks.corrstrength.stepSize=0.01;
  255. %analysis
  256. for ii=1:WB.info.groupnum(1)
  257. %load files
  258. dens=WB.outputs.(WB.info.groups(ii)).density;
  259. [cormat,pVal]=corr(dens','Type','Pearson'); %pearson pairwise correlation coefficient
  261. %save un-thresholded data
  262. WB.outputs.(WB.info.groups(ii)).networks.correlationmatrix.unthresholded=cormat;
  263. WB.outputs.(WB.info.groups(ii)).networks.correlationmatrix.pvalues=pVal;
  265. %optional: save correlation matrix heatmap
  266. %correlation matrix image
  267. figure('visible','off');
  268. clims=[-1 1];
  269. imagesc(cormat,clims);
  270. colorbar;
  271. colormap(redbluecmap);
  272. set(gcf,'Position',get(0,'Screensize'));
  273. axis('square');
  274. set(gca,'YTick',[]);
  275. set(gca,'XTick',[]);
  276. figname=strcat(WB.info.groups(ii),'_correlation.png');
  277. saveas(gcf,figname); %makes graph image
  278. close gcf
  280. %threshold
  281. cormat(pVal>=(WB.info.networks.threshold.pthresh))=0;
  282. fdr=fdr_bh(pVal,WB.info.networks.threshold.fdrthresh);
  283. cormat(fdr==0)=0;
  284. WB.outputs.(WB.info.groups(ii)).networks.correlationmatrix.thresholded=cormat;
  286. %optional: save adjacency matrix
  287. %adjacency matrix image
  288. figure('visible','off');
  289. clims=[-1 1];
  290. imagesc(cormat,clims);
  291. colorbar;
  292. colormap(redbluecmap);
  293. set(gcf,'Position',get(0,'Screensize'));
  294. axis('square');
  295. set(gca,'YTick',[]);
  296. set(gca,'XTick',[]);
  297. figname=strcat(WB.info.groups(ii),'_adjacency.png');
  298. saveas(gcf,figname); %makes graph image
  299. close gcf
  301. %correlation strength survival
  302. numsteps=((WB.info.networks.corrstrength.lastR)-(WB.info.networks.corrstrength.startR))/(WB.info.networks.corrstrength.stepSize);
  303. corsurvivalcurve=zeros(numsteps,1);
  304. tempR=WB.info.networks.corrstrength.startR;
  305. for iii=1:numsteps
  306. temp=cormat;
  307. temp(temp<tempR & temp>(-(tempR)))=0; %threshold
  308. temp(temp>tempR | temp<(-(tempR)))=1; %threshold
  309. deg=degrees_und(temp);
  310. totalconnections=sum(deg)/2; %number of connections
  311. corsurvivalcurve(iii)=totalconnections;
  312. tempR=tempR+(WB.info.networks.corrstrength.stepSize); %adjust threshold to next step
  313. end
  314. WB.outputs.(WB.info.groups(ii)).networks.corrstrengthsurvival=corsurvivalcurve;
  316. %binarize to undirected adjacency matrix
  317. cormat(cormat<(WB.info.networks.threshold.binary) & cormat>-(WB.info.networks.threshold.binary))=0;
  318. cormat(cormat>(WB.info.networks.threshold.binary) | cormat<-(WB.info.networks.threshold.binary))=1;
  319. %optional: save adjacency matrix circle plot
  320. G=graph(cormat,'upper','omitselfloops');
  321. figure('visible','off');h=plot(G,'layout','circle'); h.MarkerSize=2;
  322. regnames=WB.info.useratlas.(char(fieldnames(WB.info.useratlas)))(2:end,1);
  323. labelnode(h,(1:size(cormat,1)),regnames);
  324. set(gcf,'Position',get(0,'Screensize'));
  325. axis('square');
  326. set(gca,'YTick',[]);
  327. set(gca,'XTick',[]);
  328. figname=strcat(WB.info.groups(ii),'_circleplot.png');
  329. saveas(gcf,figname); %makes graph image
  330. close gcf
  332. %general network parameters
  333. %density
  334. dens=density_und(cormat);
  335. %degree
  336. deg=(degrees_und(cormat))';
  337. %global efficiency
  338. ge=efficiency_bin(cormat);
  339. %clustering coefficient
  340. cc=clustering_coef_bu(cormat);
  342. %save outputs
  343. WB.outputs.(WB.info.groups(ii)).networks.degrees=deg;
  344. WB.outputs.(WB.info.groups(ii)).networks.density=dens;
  345. WB.outputs.(WB.info.groups(ii)).networks.globalefficiency=ge;
  346. WB.outputs.(WB.info.groups(ii)).networks.clusteringcoeff=cc;
  347. end
  349. %clean up variable space
  350. clearvars -except WB
  352. %process complete
  353. disp('Process 3. Basic Network Analyses Complete');
  355. %% 4. Other Network Analyses
  356. % User must have the WB structure in their workspace prior to starting
  357. % this process
  358. %
  359. % Analyses:
  360. % Centrality
  361. % Targeted Node Deletion
  362. % requires targeteddeletioncurve.m (https://github.com/dterstege/TargetedNodeDeletionToolbox)
  363. % Markov Clustering
  364. % requires mcl.m and deduce_mcl_clusters.m (https://github.com/AndrasHartmann/MMCL)
  365. % Small-World-Like Properties
  366. % requires smallworldassessment.m (https://github.com/dterstege/SmallWorldAssessment)
  368. %analysis
  369. for ii=1:WB.info.groupnum(1)
  370. %load files
  371. amat=WB.outputs.(WB.info.groups(ii)).networks.correlationmatrix.thresholded;
  372. cormat=WB.outputs.(WB.info.groups(ii)).networks.correlationmatrix.unthresholded;
  373. thresh=WB.info.networks.threshold.binary;
  374. regnames=WB.info.useratlas.(char(fieldnames(WB.info.useratlas)))(2:end,1);
  376. %Katz Centrality
  377. n=size(amat,2);
  378. x0=zeros(n,1);
  379. x1=repmat((1/n),n,1);
  380. alpha=0.1; %dampening factor - adjust as needed
  381. beta=1.0; %exogenous vector
  382. t=2; %precision - adjust as needed
  383. eps=1/(10^t);
  384. iter=0;
  385. while (sum(abs(x0-x1))>eps)
  386. x0=x1;
  387. x1=(alpha*x1.*amat)+beta;
  388. iter=iter+1;
  389. end
  390. x1(x1==1)=NaN;
  391. cent=sum(x1,2,'omitnan');
  392. WB.outputs.(WB.info.groups(ii)).networks.centrality=cent;
  394. %Targeted Node Deletion
  395. [GC,E]=targeteddeletioncurve(cormat,thresh);
  396. WB.outputs.(WB.info.groups(ii)).networks.targeteddeletion.efficiency=E;
  397. WB.outputs.(WB.info.groups(ii)).networks.targeteddeletion.giantcomponent=GC;
  399. %markov clusters
  400. sbeG=mcl(amat, 0, 0, 0, 0, n); %no self loops
  401. [numclusts,clustmembers]=deduce_mcl_clusters(sbeG,regnames);
  402. WB.outputs.(WB.info.groups(ii)).networks.markov.numberofclusters=numclusts;
  403. WB.outputs.(WB.info.groups(ii)).networks.markov.clustermembers=clustmembers;
  405. %Small World Properties
  406. [SW,~,C,CR,E,ER]=smallworldassessment(amat,thresh);
  408. %save
  409. WB.outputs.(WB.info.groups(ii)).networks.smallworld.status=SW;
  410. WB.outputs.(WB.info.groups(ii)).networks.smallworld.clustering=C;
  411. WB.outputs.(WB.info.groups(ii)).networks.smallworld.randomclustering=CR;
  412. WB.outputs.(WB.info.groups(ii)).networks.smallworld.efficiency=E;
  413. WB.outputs.(WB.info.groups(ii)).networks.smallworld.randomefficiency=ER;
  414. end
  416. %clean up variable space
  417. clearvars -except WB
  419. %process complete
  420. disp('Process 4. Other Network Analyses Complete');
  422. %% X. Save Structure to .mat File
  423. %keep file for easy data access later
  425. save('WB.mat','-struct','WB');
  427. disp('Process X. Save Structure to a .mat File Complete');
  429. %% Y. Load .mat File
  430. % navigate to folder containing 'WB.mat'
  432. load('WB.mat')
  433. w=whos;
  434. for ii=1:length(w)
  435. WB.(w(ii).name)=eval(w(ii).name);
  436. clear w(ii).name
  437. end
  439. %clean up variable space
  440. clearvars -except WB
  442. disp('Process Y. Load .mat File Complete');

WholeBrainAnalysis_v101.m, under CC-BY-4.0 · at the source

Overview

Authors: Dylan J Terstege1, Jonathan R Epp1
ORCID iDs: Dylan J Terstege, Jonathan R Epp
  1. Department of Cell Biology and Anatomy, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada
Institutions: University of Calgary (Canada); Hotchkiss Brain Institute (Canada)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 9, article e0455252026
Dates: received 4 March 2025; accepted 5 January 2026; published online 3 February 2026; in print 4 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.0455-25.2026 · PMID 41633836 · PMCID PMC12962780 · OpenAlex W7127321809
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Alzheimer's disease, enriched housing, perineuronal nets, retrosplenial cortex
MeSH: Alzheimer Disease*, Cognition*, Environment*, Gyrus Cinguli*, Parvalbumins*, Amyloid beta-Protein Precursor, Animals, Disease Models, Animal, Female, Housing, Animal, Humans, Interneurons, Mice, Mice, Transgenic, Perineuronal Nets, Plant Lectins, Receptors, N-Acetylglucosamine (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 103 references in the paper
Research resources: RRID:Addgene_28306

Abstract

The rate of cognitive decline in Alzheimer's disease (AD) varies considerably from person to person. Numerous epidemiological studies point to the protective effects of cognitive, social, and physical enrichment as potential mediators of cognitive decline in AD; however, there is much debate as to the mechanism underlying these protective effects. The retrosplenial cortex (RSC) is one of the earliest brain regions with impaired functions during AD pathogenesis, and its activity is affected by cognitive, social, and physical stimulation, making it a particularly interesting region to investigate the influences of an enriched lifestyle on AD pathogenesis. In the current study, we use the 5xFAD mouse mode of AD to examine the impact of enriched housing conditions on cognitive function in AD and the viability of a particularly vulnerable cell population within the RSC—parvalbumin interneurons (PV-INs). Enriched housing conditions improved cognitive performance in female 5xFAD mice. These changes in cognitive performance coincided with restored functional connectivity of the RSC and preserved PV-IN density within this region. Along with preserved PV-IN density, there was an increase in the density of Wisteria floribunda agglutinin-positive perineuronal nets (WFA+ PNNs) across the RSC of 5xFAD mice housed in enriched conditions. Direct manipulation of WFA+ PNNs revealed that these extracellular matrix structures protect PV-INs from amyloid toxicity and may be the mechanisms underlying the protective effects of enrichment. Together, these results provide support for the WFA+ PNN-mediated maintenance of PV-INs in the RSC as a potential mechanism mediating the protective effects of enrichment against cognitive decline in AD.

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Zenodo 17569435

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Statistics and data visualization”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (7 files), Signal Processing Toolbox (3 files), Brain Connectivity Toolbox (2 files), fdr_bh (Benjamini-Hochberg FDR) (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
15 files
At the source: doi.org/10.5281/zenodo.17569435

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 17 MeSH terms, 103 references, 1 RRID.

Cite

This paper

Terstege, D. J., & Epp, J. R. (2026). Environmental Enrichment Preserves Retrosplenial Parvalbumin Density and Cognitive Function in Female 5xFAD Mice. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(9), e0455252026. https://doi.org/10.1523/jneurosci.0455-25.2026

BibTeX

@article{terstege2026environmental,
author = {Terstege, Dylan J and Epp, Jonathan R},
title = {{Environmental Enrichment Preserves Retrosplenial Parvalbumin Density and Cognitive Function in Female 5xFAD Mice}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = mar,
volume = {46},
number = {9},
pages = {e0455252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.0455-25.2026},
url = {https://doi.org/10.1523/jneurosci.0455-25.2026},
pmid = {41633836},
pmcid = {PMC12962780}
}

RIS

TY - JOUR
AU - Terstege, Dylan J
AU - Epp, Jonathan R
TI - Environmental Enrichment Preserves Retrosplenial Parvalbumin Density and Cognitive Function in Female 5xFAD Mice
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/03/04
VL - 46
IS - 9
SP - e0455252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.0455-25.2026
UR - https://doi.org/10.1523/jneurosci.0455-25.2026
LA - en
ER -

CSL-JSON

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