OSCR

Adult lifespan effects on functional specialization along the hippocampal long axis.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Methods › Data acquisition and processing › fMRI preprocessing ↔ fmriqa.py, lines 253–332 · score 0.51 · framewise displacement, DVARS, derivative, FD, timepoints, scores

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Python · 336 lines · 11 KB · no license · 1 match

  1. #!/usr/bin/env python
  2. """
  3. fMRI quality control
  4. - adapted from fsld_raw.R and fBIRN QA tools
  6. USAGE: fmriqa.py bold_mcf.nii.gz <TR>
  7. """
  9. import ctypes, sys, os
  11. flags = sys.getdlopenflags()
  12. sys.setdlopenflags(flags|ctypes.RTLD_GLOBAL)
  14. import numpy as N
  15. import nibabel as nib
  16. from compute_fd import *
  17. from statsmodels.tsa.tsatools import detrend
  18. import statsmodels.api
  19. import matplotlib
  20. matplotlib.use('Agg')
  21. import matplotlib.pyplot as plt
  22. import sklearn.cross_validation
  23. from matplotlib.backends.backend_pdf import PdfPages
  24. from mk_slice_mosaic import *
  25. from matplotlib.mlab import psd
  26. from plot_timeseries import plot_timeseries
  27. from MAD import MAD
  28. from mk_report import mk_report
  30. sys.setdlopenflags(flags)
  32. # thresholds for scrubbing and spike detection
  33. FDthresh=0.5
  34. DVARSthresh=0.5
  35. AJKZ_thresh=25
  36. # number of timepoints forward and back to scrub
  37. nback=1
  38. nforward=2
  41. def error_and_exit(msg):
  42. print msg
  43. sys.stdout.write(__doc__)
  44. sys.exit(2)
  48. def main():
  49. verbose=True
  51. if len(sys.argv)>2:
  52. infile=sys.argv[1]
  53. TR=float(sys.argv[2])
  54. else:
  55. error_and_exit('')
  56. #infile='/corral-repl/utexas/poldracklab/openfmri/shared2/ds105/sub001/BOLD/task001_run001/bold_mcf.nii.gz'
  57. #TR=2.5
  59. qadir=fmriqa(infile,TR,verbose=verbose)
  61. def fmriqa(infile,TR,outdir=None,maskfile=None,motfile=None,verbose=False,plot_data=True):
  62. save_sfnr=True
  64. if os.path.dirname(infile)=='':
  65. basedir=os.getcwd()
  66. infile=os.path.join(basedir,infile)
  67. elif os.path.dirname(infile)=='.':
  68. basedir=os.getcwd()
  69. infile=os.path.join(basedir,infile.replace('./',''))
  70. else:
  71. basedir=os.path.dirname(infile)
  73. if outdir==None:
  74. outdir=basedir
  76. qadir=os.path.join(outdir,'QA')
  78. if not infile.find('mcf.nii.gz')>0:
  79. error_and_exit('infile must be of form XXX_mcf.nii.gz')
  81. if not os.path.exists(infile):
  82. error_and_exit('%s does not exist!'%infile)
  84. if maskfile==None:
  85. maskfile=infile.replace('mcf.nii','mcf_brain_mask.nii')
  87. if not os.path.exists(maskfile):
  88. error_and_exit('%s does not exist!'%maskfile)
  90. if motfile==None:
  91. motfile=infile.replace('mcf.nii.gz','mcf.par')
  92. if not os.path.exists(motfile):
  93. error_and_exit('%s does not exist!'%motfile)
  96. if not os.path.exists(qadir):
  97. os.mkdir(qadir)
  98. else:
  99. print 'QA dir already exists - overwriting!'
  102. if verbose:
  103. print 'infile:',infile
  104. print 'maskfile:',maskfile
  105. print 'motfile:',motfile
  106. print 'outdir:',outdir
  107. print 'computing image stats'
  109. img=nib.load(infile)
  110. imgdata=img.get_data()
  112. nslices=imgdata.shape[2]
  113. ntp=imgdata.shape[3]
  115. maskimg=nib.load(maskfile)
  116. maskdata=maskimg.get_data()
  117. maskvox=N.where(maskdata>0)
  118. nonmaskvox=N.where(maskdata==0)
  119. if verbose:
  120. print 'nmaskvox:',len(maskvox[0])
  122. # load motion parameters and compute FD and identify bad vols for
  123. # potential scrubbing (ala Power et al.)
  125. motpars=N.loadtxt(motfile)
  126. fd=compute_fd(motpars)
  127. N.savetxt(os.path.join(qadir,'fd.txt'),fd)
  129. voxmean=N.mean(imgdata,3)
  130. voxstd=N.std(imgdata,3)
  131. voxcv=voxstd/N.abs(voxmean)
  132. voxcv[N.isnan(voxcv)]=0
  133. voxcv[voxcv>1]=1
  135. # compute timepoint statistics
  138. maskmedian=N.zeros(imgdata.shape[3])
  139. maskmean=N.zeros(imgdata.shape[3])
  140. maskmad=N.zeros(imgdata.shape[3])
  141. maskcv=N.zeros(imgdata.shape[3])
  142. imgsnr=N.zeros(imgdata.shape[3])
  144. for t in range(imgdata.shape[3]):
  145. tmp=imgdata[:,:,:,t]
  146. tmp_brain=tmp[maskvox]
  147. tmp_nonbrain=tmp[nonmaskvox]
  148. maskmad[t]=MAD(tmp_brain)
  149. maskmedian[t]=N.median(tmp_brain)
  150. maskmean[t]=N.mean(tmp_brain)
  151. maskcv[t]=maskmad[t]/maskmedian[t]
  152. imgsnr[t]=maskmean[t]/N.std(tmp_nonbrain)
  155. # perform Greve et al./fBIRN spike detection
  156. #1. Remove mean and temporal trend from each voxel.
  157. #2. Compute temporal Z-score for each voxel.
  158. #3. Average the absolute Z-score (AAZ) within a each slice and time point separately.
  159. # This gives a matrix with number of rows equal to the number of slices (nSlices)
  160. # and number of columns equal to the number of time points (nFrames).
  161. #4. Compute new Z-scores using a jackknife across the slices (JKZ). For a given time point,
  162. # remove one of the slices, compute the average and standard deviation of the AAZ across
  163. # the remaining slices. Use these two numbers to compute a Z for the slice left out
  164. # (this is the JKZ). The final Spike Measure is the absolute value of the JKZ (AJKZ).
  165. # Repeat for all slices. This gives a new nSlices-by-nFrames matrix (see Figure 8).
  166. # This procedure tends to remove components that are common across slices and so rejects motion.
  167. if verbose:
  168. print 'computing spike stats'
  170. detrended_zscore=N.zeros(imgdata.shape)
  171. detrended_data=N.zeros(imgdata.shape)
  173. for i in range(len(maskvox[0])):
  174. tmp=imgdata[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]
  175. tmp_detrended=detrend(tmp)
  176. detrended_data[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=tmp_detrended
  177. detrended_zscore[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=(tmp_detrended - N.mean(tmp_detrended))/N.std(tmp_detrended)
  179. loo=sklearn.cross_validation.LeaveOneOut(nslices)
  180. AAZ=N.zeros((nslices,ntp))
  182. for s in range(nslices):
  183. for t in range(ntp):
  184. AAZ[s,t]=N.mean(N.abs(detrended_zscore[:,:,s,t]))
  186. JKZ=N.zeros((nslices,ntp))
  188. if verbose:
  189. print 'computing outliers'
  191. for train,test in loo:
  192. for tp in range(ntp):
  193. train_mean=N.mean(AAZ[train,tp])
  194. train_std=N.std(AAZ[train,tp])
  195. JKZ[test,tp]=(AAZ[test,tp] - train_mean)/train_std
  197. AJKZ=N.abs(JKZ)
  198. spikes=[]
  199. if N.max(AJKZ)>AJKZ_thresh:
  200. print 'Possible spike: Max AJKZ = %f'%N.max(AJKZ)
  201. spikes=N.where(N.max(AJKZ,0)>AJKZ_thresh)[0]
  202. if len(spikes)>0:
  203. N.savetxt(os.path.join(qadir,'spikes.txt'),spikes)
  205. voxmean_detrended=N.mean(detrended_data,3)
  206. voxstd_detrended=N.std(detrended_data,3)
  207. voxsfnr=voxmean/voxstd
  208. meansfnr=N.mean(voxsfnr[maskvox])
  210. # create plots
  212. #
  213. #imgdata_flat=imgdata.reshape(N.prod(imgdata.shape))
  214. #imgdata_nonzero=imgdata_flat[imgdata_flat>0.0]
  216. scaledmean=(maskmean - N.mean(maskmean))/N.std(maskmean)
  217. mean_running_diff=N.zeros(maskmad.shape)
  218. mean_running_diff=(maskmean[1:]-maskmean[:-1])/((maskmean[1:]+maskmean[:-1])/2.0)
  219. DVARS=N.zeros(fd.shape)
  220. DVARS[1:]=N.sqrt(mean_running_diff**2)*100.0
  221. N.savetxt(os.path.join(qadir,'dvars.txt'),DVARS)
  223. badvol_index_orig=N.where((fd>FDthresh)*(DVARS>DVARSthresh))[0]
  224. #print badvol_index_orig
  225. badvols=N.zeros(len(DVARS))
  226. badvols[badvol_index_orig]=1
  227. badvols_expanded=badvols.copy()
  228. for i in badvol_index_orig:
  229. if i>(nback-1):
  230. start=i-nback
  231. else:
  232. start=0
  233. if i<(len(badvols)-nforward):
  234. end=i+nforward+1
  235. else:
  236. end=len(badvols)
  237. #print i,start,end
  238. badvols_expanded[start:end]=1
  239. badvols_expanded_index=N.where(badvols_expanded>0)[0]
  240. #print badvols_expanded_index
  241. if len(badvols_expanded_index)>0:
  242. N.savetxt(os.path.join(qadir,'scrubvols.txt'),badvols_expanded_index,fmt='%d')
  244. # make scrubing design matrix - one colum per scrubbed timepoint
  245. scrubdes=N.zeros((len(DVARS),len(badvols_expanded_index)))
  246. for i in range(len(badvols_expanded_index)):
  247. scrubdes[badvols_expanded_index[i],i]=1
  248. N.savetxt(os.path.join(qadir,'scrubdes.txt'),scrubdes,fmt='%d')
  250. else:
  251. scrubdes=[]
  253. # save out complete confound file
  254. confound_mtx=N.zeros((len(DVARS),14))
  255. confound_mtx[:,0:6]=motpars
  256. confound_mtx[1:,6:12]=motpars[:-1,:]-motpars[1:,:] # derivs
  257. confound_mtx[:,12]=fd
  258. confound_mtx[:,13]=DVARS
  259. if not scrubdes==[]:
  260. confound_mtx=N.hstack((confound_mtx,scrubdes))
  262. N.savetxt(os.path.join(qadir,'confound.txt'),confound_mtx)
  264. #plot_timeseries(scaledmean,'Mean in-mask signal (Z-scored)',
  265. # os.path.join(qadir,'scaledmaskmean.png'),spikes,'Potential spikes')
  267. datavars={'imgsnr':imgsnr,'meansfnr':meansfnr,'spikes':spikes,'badvols':badvols_expanded_index}
  269. if plot_data:
  270. print 'before plot'
  271. trend=plot_timeseries(maskmean,'Mean signal (unfiltered)',os.path.join(qadir,'maskmean.png'),
  272. plottrend=True,ylabel='Mean MR signal')
  273. print 'after plot'
  274. datavars['trend']=trend
  275. plot_timeseries(maskmad,'Median absolute deviation (robust SD)',
  276. os.path.join(qadir,'mad.png'),ylabel='MAD')
  278. plot_timeseries(DVARS,'DVARS (root mean squared signal derivative over brain mask)',
  279. os.path.join(qadir,'DVARS.png'),plotline=0.5,ylabel='DVARS')
  281. plot_timeseries(fd,'Framewise displacement',os.path.join(qadir,'fd.png'),
  282. badvols_expanded_index,'Timepoints to scrub (%d total)'%len(badvols),
  283. plotline=0.5,ylims=[0,1],ylabel='FD')
  285. psd=matplotlib.mlab.psd(maskmean,NFFT=128,noverlap=96,Fs=1/TR)
  287. plt.clf()
  288. fig=plt.figure(figsize=[10,3])
  289. fig.subplots_adjust(bottom=0.15)
  290. plt.plot(psd[1][2:],N.log(psd[0][2:]))
  291. plt.title('Log power spectrum of mean signal across mask')
  292. plt.xlabel('frequency (secs)')
  293. plt.ylabel('log power')
  294. plt.savefig(os.path.join(qadir,'meanpsd.png'),bbox_inches='tight')
  295. plt.close()
  297. plt.clf()
  298. plt.imshow(AJKZ,vmin=0,vmax=AJKZ_thresh)
  299. plt.xlabel('timepoints')
  300. plt.ylabel('slices')
  301. plt.title('Spike measure (absolute jackknife Z)')
  302. plt.savefig(os.path.join(qadir,'spike.png'),bbox_inches='tight')
  303. plt.close()
  305. if img.shape[0]<img.shape[1] and img.shape[0]<img.shape[2]:
  306. orientation='saggital'
  307. else:
  308. orientation='axial'
  310. mk_slice_mosaic(voxmean,os.path.join(qadir,'voxmean.png'),'Image mean (with mask)',contourdata=maskdata)
  311. mk_slice_mosaic(voxcv,os.path.join(qadir,'voxcv.png'),'Image CV')
  312. mk_slice_mosaic(voxsfnr,os.path.join(qadir,'voxsfnr.png'),'Image SFNR')
  315. mk_report(infile,qadir,datavars)
  318. # def save_vars(infile,qadir,datavars):
  319. datafile=os.path.join(qadir,'qadata.csv')
  320. f=open(datafile,'w')
  321. f.write('SNR,%f\n'%N.mean(datavars['imgsnr']))
  322. f.write('SFNR,%f\n'%datavars['meansfnr'])
  323. #f.write('drift,%f\n'%datavars['trend'].params[1])
  324. f.write('nspikes,%d\n'%len(datavars['spikes']))
  325. f.write('nscrub,%d\n'%len(datavars['badvols']))
  326. f.close()
  328. if save_sfnr:
  329. sfnrimg=nib.Nifti1Image(voxsfnr,img.get_affine())
  330. sfnrimg.to_filename(os.path.join(qadir,'voxsfnr.nii.gz'))
  332. return qadir
  334. if __name__=='__main__':
  335. main()

fmriqa.py at commit 2b217da, no license · at the source

Overview

Authors: Caitlin R. Bowman1, Cara I. Charles1, Saisha M. Birr1
ORCID iDs: Caitlin R. Bowman
  1. Department of Psychological and Brain Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
Institutions: University of Wisconsin–Milwaukee (United States)
Journal: Frontiers in cognition, volume 5, article 1767179
Dates: received 13 December 2025; accepted 6 April 2026; published online 5 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fcogn.2026.1767179 · PMID 42338776 · PMCID PMC13271125 · OpenAlex W4403151918
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.10.04.616732
Status: code verified
Categories: fMRI (modality), healthy (population), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: episodic memory, fMRI, functional connectivity, healthy aging, hippocampus, lifespan
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (R01 AG098263)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

Introduction: There has been increasing attention to differences in function along the hippocampal long axis, with the posterior hippocampus proposed to have more variable signals that are well-suited to representing idiosyncratic details in memory, and the anterior hippocampus having less dynamic signals that are well-suited to integration. Whether long axis functional specialization persists into older age is not well-understood, despite known age-related declines in the level of detail in memories.

Methods: We used a large database of resting state fMRI data (n = 337 humans of both sexes included) from across the adult lifespan (ages 18–88) to determine the degree of functional differentiation across the hippocampal posterior-anterior axis. Our first approach was to measure the correlation of signals within hippocampal subregions. Our second approach was to measure functional connectivity between hippocampal subregions and the rest of the brain. For both approaches, we tested how well functional differences along the hippocampal long axis accounted for individual and age differences in episodic memory.

Results: Within the hippocampus, we found a more positive age slope (i.e., increasing similarity of signals) for the most posterior hippocampal region compared to the intermediate and anterior region, consistent with the posterior hippocampus losing some of its heterogeneous signaling in older age. Patterns of whole-brain connectivity showed significant differences in the age trajectories between hippocampal subregions for a number of target regions, including several frontal connections. Yet we did not find strong evidence that either within-hippocampal signals or differences in functional connectivity were associated with age-related episodic memory decline.

Conclusion: Age differences in hippocampal long axis functional organization were apparent during rest but were limited in how well they accounted for memory decline.

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 1 match between paragraphs and lines of code.

poldrack/fmriqa

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2b217dad803e5fc0e3ddca3d7099d99b3b1db089, 13 July 2015
Languages: Python (8)
Size: 10 files, 8 scripts
Software Heritage: archived
Found in: the text, “fMRI preprocessing”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), Matplotlib (3 files), NiBabel (3 files), statsmodels (2 files), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
9 files
At the source: github.com/poldrack/fmriqa

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;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data availability statement

The original contributions presented in the study are publicly available. This data can be found here: https://osf.io/qba8n/.

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

Data and code availability

The raw data come from a publicly available dataset (https://www.cam-can.org). Intervoxel similarity values (IVS), functional connectivity values, scores on the behavioral tasks used, and nuisance covariates for individual subjects are publicly available through the Open Science Framework (https://osf.io/qba8n/). Analytic code is also available.

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 2, 28 September 2026

  • Authors: added Caitlin R. Bowman (0000-0002-5833-3591); removed Caitlin R. Bowman

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 6 keywords, 1 funder, 93 references.

Cite

This paper

Bowman, C. R., Charles, C. I., & Birr, S. M. (2026). Adult lifespan effects on functional specialization along the hippocampal long axis. Frontiers in cognition, 5, 1767179. https://doi.org/10.3389/fcogn.2026.1767179

BibTeX

@article{bowman2026adult,
author = {Bowman, Caitlin R. and Charles, Cara I. and Birr, Saisha M.},
title = {{Adult lifespan effects on functional specialization along the hippocampal long axis}},
journal = {Frontiers in cognition},
year = {2026},
month = may,
volume = {5},
pages = {1767179},
publisher = {Frontiers Media SA},
issn = {2813-4532},
doi = {10.3389/fcogn.2026.1767179},
url = {https://doi.org/10.3389/fcogn.2026.1767179},
pmid = {42338776},
pmcid = {PMC13271125}
}

RIS

TY - JOUR
AU - Bowman, Caitlin R.
AU - Charles, Cara I.
AU - Birr, Saisha M.
TI - Adult lifespan effects on functional specialization along the hippocampal long axis
T2 - Frontiers in cognition
J2 - Front Cognit
PY - 2026
DA - 2026/05/05
VL - 5
SP - 1767179
SN - 2813-4532
PB - Frontiers Media SA
DO - 10.3389/fcogn.2026.1767179
UR - https://doi.org/10.3389/fcogn.2026.1767179
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fcogn.2026.1767179",
"type": "article-journal",
"title": "Adult lifespan effects on functional specialization along the hippocampal long axis",
"container-title": "Frontiers in cognition",
"author": [
{
"family": "Bowman",
"given": "Caitlin R."
},
{
"family": "Charles",
"given": "Cara I."
},
{
"family": "Birr",
"given": "Saisha M."
}
],
"container-title-short": "Front Cognit",
"volume": "5",
"page": "1767179",
"DOI": "10.3389/fcogn.2026.1767179",
"PMID": "42338776",
"PMCID": "PMC13271125",
"ISSN": "2813-4532",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fcogn.2026.1767179",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
5
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/hbm.70475 [code]
The Development of Hippocampal-Cortical Functional Connectivity in Infants and Toddlers.
Journal: Human brain mapping
In common: fMRI, 8 references
[2] doi:10.3389/fnbeh.2026.1895371 [code]
Discrete and differentiated encoding of distinct components of spatial experience occurs within the proximodistal subfields of the dorsoventral axis of the hippocampus.
Journal: Frontiers in behavioral neuroscience
In common: Matplotlib, NumPy, 7 references
[3] doi:10.1371/journal.pcbi.1013488 [code]
Evaluating place cell detection methods in Rats and Humans: Implications for cross-species spatial coding.
Journal: PLoS computational biology
In common: statsmodels, scikit-learn, Matplotlib, 1 other tool, 5 references
[4] doi:10.1002/hipo.70124 [code]
Association Between Anterior Hippocampal Gyrification and Episodic Memory Performance in Neurotypical Young Adults.
Journal: Hippocampus
In common: NiBabel, scikit-learn, Matplotlib, 1 other tool, healthy, cognitive, 3 references
[5] doi:10.21203/rs.3.rs-9326213/v1 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: Research Square (preprint)
In common: NiBabel, scikit-learn, Matplotlib, 1 other tool, fMRI, cognitive, 5 references
[6] doi:10.64898/2026.03.09.710558 [code]
Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain
Journal: bioRxiv (preprint)
In common: NiBabel, scikit-learn, Matplotlib, 1 other tool, fMRI, cognitive, 5 references
[7] doi:10.1002/agm2.70073 [code]
Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex.
Journal: Aging medicine (Milton (N.S.W))
In common: NiBabel, statsmodels, scikit-learn, 2 other tools, fMRI, 3 references
[8] doi:10.1038/s41467-026-71568-9 [code]
Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula.
Journal: Nature communications
In common: NiBabel, statsmodels, scikit-learn, 2 other tools, cognitive, 4 references
[9] doi:10.7554/elife.107933 [code]
Modality-agnostic decoding of vision and language from fMRI.
Journal: eLife
In common: NiBabel, statsmodels, scikit-learn, 2 other tools, fMRI, cognitive, 4 references
[10] doi:10.7554/elife.107273
Neural representation of time across complementary reference frames.
Journal: eLife
In common: cognitive, 6 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.