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All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex.

Code ↔ Paper

7 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 7 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data analysis › Multivariate classification › Experimental questions › Relative decoding accuracy using spectral frequency power or phase vs. voltage ↔ decode_during_preprocessing.r, lines 1–59 · score 0.85 · 60–200 Hz, 13–30 Hz, 30–60 Hz, 8–12 Hz, high gamma, 4–7 Hz
  2. [2] § Results › Relative decoding accuracy using spectral frequency power or phase vs. voltage ↔ decode_during_preprocessing.r, lines 1–59 · score 0.83 · 60–200 Hz, 13–30 Hz, 30–60 Hz, 8–12 Hz, frequency power, high gamma
  3. [3] § Methods › Data analysis › Preprocessing: ECoG ↔ decode_during_preprocessing.m, lines 231–308 · score 0.80 · median power, Decibel normalization, 4–200 Hz, cycle, timefreq, wavelet
  4. [4] § Results › Relative decoding accuracy using spectral frequency power or phase vs. voltage ↔ decode_during_preprocessing.r, lines 101–178 · score 0.75 · 60–200 Hz, 13–30 Hz, 30–60 Hz, 8–12 Hz, high gamma, 4–7 Hz
  5. [5] § Methods › Patients ↔ count_electrodes.m, the whole file · a weak match · score 0.69 · electrodes implanted, right hemisphere, left hemisphere, ventral, patient, temporal
  6. [6] § Methods › Data analysis › Multivariate classification › Experimental questions › Does the time-frequency semantic code change dynamically? ↔ ECoG_Data_Prep-master/data/targets/similarity/semantic/Dilkina_Normalized/raw/SetupDilkineaStructure.R, lines 162–213 · score 0.55 · cosine distance, hierarchical clustering, hclust, matrix, semantic
  7. [7] § Methods › Data analysis › Multivariate classification › Experimental questions › Does the time-frequency semantic code change dynamically? ↔ ECoG_Data_Prep-master/data/targets/similarity/semantic/Dilkina_Raw/raw/SetupDilkineaStructure.R, lines 155–206 · score 0.55 · cosine distance, hierarchical clustering, hclust, matrix, semantic

Paper

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

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The authors' code

R · 178 lines · 11 KB · no license · 3 matches

  1. function(path,y=c(rep(0,times=50),rep(1,times=50)),wins=c(1:166)){
  2. # Runs decoding during preprocessing (all-frequency power, band-specific power, all-frequency
  3. # phase, band-specific phase, and voltage) and saves output as .Rdata.
  4. # path - path to /derivatives folder containing .csvs of power, decibel-normalised power and phase for one participant (e.g. "/group/mlr-lab/Saskia/ECoG_LASSO/derivatives/wavelet/sub-01/")
  5. # y - labels vector. For living/nonliving data, averaged across repeated
  6. # presentations of the same stimulus, this is y=c(rep(0,times=50),rep(1,times=50))
  7. # wins - times at which to decode: wins <- seq(1:166)
  9. setwd(path)
  10. # define output path
  11. outpath <- gsub("/wavelet/","/modelfit/",path)
  12. dir.create(outpath, recursive=TRUE)
  14. # # 1. POWER
  15. #
  16. # # load
  17. # message("Loading power data at each preprocessing stage...")
  18. # raw <- read.csv("raw_power.csv",header=F)
  19. # raw <- as.matrix(raw)
  20. # filtered <- read.csv("filtered_power.csv",header=F)
  21. # filtered <- as.matrix(filtered)
  22. # channelsrejected <- read.csv("channelsrejected_power.csv",header=F)
  23. # channelsrejected <- as.matrix(channelsrejected)
  24. # rereferenced <- read.csv("rereferenced_power.csv",header=F)
  25. # rereferenced <- as.matrix(rereferenced)
  26. # trialsrejected <- read.csv("trialsrejected_power.csv",header=F)
  27. # trialsrejected <- as.matrix(trialsrejected)
  28. #
  29. # # decode all frequencies
  30. # powerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:60))
  31. # save(powerddp,file=paste(outpath,"ddp_power_results.Rdata",sep=""))
  32. # rm(powerddp)
  33. #
  34. # # decode theta (4-7 Hz)
  35. # thetapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:11))
  36. # save(thetapowerddp,file=paste(outpath,"ddp_theta_power_results.Rdata",sep=""))
  37. # rm(thetapowerddp)
  38. #
  39. # # decode alpha (8-12 Hz)
  40. # alphapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(12:18))
  41. # save(alphapowerddp,file=paste(outpath,"ddp_alpha_power_results.Rdata",sep=""))
  42. # rm(alphapowerddp)
  43. #
  44. # # decode beta (13-30 Hz)
  45. # betapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(19:31))
  46. # save(betapowerddp,file=paste(outpath,"ddp_beta_power_results.Rdata",sep=""))
  47. # rm(betapowerddp)
  48. #
  49. # # decode gamma (30-60 Hz)
  50. # gammapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(32:41))
  51. # save(gammapowerddp,file=paste(outpath,"ddp_gamma_power_results.Rdata",sep=""))
  52. # rm(gammapowerddp)
  53. #
  54. # # decode high gamma (60-200 Hz)
  55. # highgammapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(42:60))
  56. # save(highgammapowerddp,file=paste(outpath,"ddp_high_gamma_power_results.Rdata",sep=""))
  57. # rm(highgammapowerddp)
  58. #
  59. # rm(raw,filtered,channelsrejected,rereferenced,trialsrejected)
  61. # 2. DECIBEL-NORMALISED POWER
  63. # load
  64. message("Loading decibel-normalised power data at each preprocessing stage...")
  65. raw <- read.csv("raw_dBpower.csv",header=F)
  66. raw <- as.matrix(raw)
  67. filtered <- read.csv("filtered_dBpower.csv",header=F)
  68. filtered <- as.matrix(filtered)
  69. channelsrejected <- read.csv("channelsrejected_dBpower.csv",header=F)
  70. channelsrejected <- as.matrix(channelsrejected)
  71. rereferenced <- read.csv("rereferenced_dBpower.csv",header=F)
  72. rereferenced <- as.matrix(rereferenced)
  73. trialsrejected <- read.csv("trialsrejected_dBpower.csv",header=F)
  74. trialsrejected <- as.matrix(trialsrejected)
  76. # decode all frequencies
  77. dBpowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:60))
  78. save(dBpowerddp,file=paste(outpath,"ddp_dB_power_results.Rdata",sep=""))
  79. rm(dBpowerddp)
  81. # decode theta (4-7 Hz)
  82. dBthetapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:11))
  83. save(dBthetapowerddp,file=paste(outpath,"ddp_dB_theta_power_results.Rdata",sep=""))
  84. rm(dBthetapowerddp)
  86. # decode alpha (8-12 Hz)
  87. dBalphapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(12:18))
  88. save(dBalphapowerddp,file=paste(outpath,"ddp_dB_alpha_power_results.Rdata",sep=""))
  89. rm(dBalphapowerddp)
  91. # decode beta (13-30 Hz)
  92. dBbetapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(19:31))
  93. save(dBbetapowerddp,file=paste(outpath,"ddp_dB_beta_power_results.Rdata",sep=""))
  94. rm(dBbetapowerddp)
  96. # decode gamma (30-60 Hz)
  97. dBgammapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(32:41))
  98. save(dBgammapowerddp,file=paste(outpath,"ddp_dB_gamma_power_results.Rdata",sep=""))
  99. rm(dBgammapowerddp)
  101. # decode high gamma (60-200 Hz)
  102. dBhighgammapowerddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(42:60))
  103. save(dBhighgammapowerddp,file=paste(outpath,"ddp_dB_high_gamma_power_results.Rdata",sep=""))
  104. rm(dBhighgammapowerddp)
  106. rm(raw,filtered,channelsrejected,rereferenced,trialsrejected)
  108. # 3. PHASE
  110. # load
  111. message("Loading phase data at each preprocessing stage...")
  112. raw <- read.csv("raw_phase.csv",header=F)
  113. raw <- as.matrix(raw)
  114. filtered <- read.csv("filtered_phase.csv",header=F)
  115. filtered <- as.matrix(filtered)
  116. channelsrejected <- read.csv("channelsrejected_phase.csv",header=F)
  117. channelsrejected <- as.matrix(channelsrejected)
  118. rereferenced <- read.csv("rereferenced_phase.csv",header=F)
  119. rereferenced <- as.matrix(rereferenced)
  120. trialsrejected <- read.csv("trialsrejected_phase.csv",header=F)
  121. trialsrejected <- as.matrix(trialsrejected)
  123. # decode all frequencies
  124. phaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:60))
  125. save(phaseddp,file=paste(outpath,"ddp_phase_results.Rdata",sep=""))
  126. rm(phaseddp)
  128. # decode theta (4-7 Hz)
  129. thetaphaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:11))
  130. save(thetaphaseddp,file=paste(outpath,"ddp_theta_phase_results.Rdata",sep=""))
  131. rm(thetaphaseddp)
  133. # decode alpha (8-12 Hz)
  134. alphaphaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(12:18))
  135. save(alphaphaseddp,file=paste(outpath,"ddp_alpha_phase_results.Rdata",sep=""))
  136. rm(alphaphaseddp)
  138. # decode beta (13-30 Hz)
  139. betaphaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(19:31))
  140. save(betaphaseddp,file=paste(outpath,"ddp_beta_phase_results.Rdata",sep=""))
  141. rm(betaphaseddp)
  143. # decode gamma (30-60 Hz)
  144. gammaphaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(32:41))
  145. save(gammaphaseddp,file=paste(outpath,"ddp_gamma_phase_results.Rdata",sep=""))
  146. rm(gammaphaseddp)
  148. # decode high gamma (60-200 Hz)
  149. highgammaphaseddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="f", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(42:60))
  150. save(highgammaphaseddp,file=paste(outpath,"ddp_high_gamma_phase_results.Rdata",sep=""))
  151. rm(highgammaphaseddp)
  153. rm(raw,filtered,channelsrejected,rereferenced,trialsrejected)
  155. # 4. VOLTAGE
  157. # load
  158. message("Loading voltage data at each preprocessing stage...")
  159. raw <- read.csv("raw_voltage.csv",header=F)
  160. raw <- as.matrix(raw)
  161. filtered <- read.csv("filtered_voltage.csv",header=F)
  162. filtered <- as.matrix(filtered)
  163. channelsrejected <- read.csv("channelsrejected_voltage.csv",header=F)
  164. channelsrejected <- as.matrix(channelsrejected)
  165. rereferenced <- read.csv("rereferenced_voltage.csv",header=F)
  166. rereferenced <- as.matrix(rereferenced)
  167. trialsrejected <- read.csv("trialsrejected_voltage.csv",header=F)
  168. trialsrejected <- as.matrix(trialsrejected)
  170. # decode voltage
  171. voltageddp <- fit.models.during.preprocessing(raw=raw, filtered=filtered, channelsrejected=channelsrejected, rereferenced=rereferenced, trialsrejected=trialsrejected, tp="v", y=y, wins=wins, ho = 5, wsz = 50, a = 1, ntpts = 166, nf = c(1:60))
  172. save(voltageddp,file=paste(outpath,"ddp_voltage_results.Rdata",sep=""))
  173. rm(voltageddp)
  175. rm(raw,filtered,channelsrejected,rereferenced,trialsrejected)
  177. message("Done!")
  178. }

decode_during_preprocessing.r at commit 64c1222, no license · at the source

Overview

Authors: Saskia L. Frisby1, Ajay D. Halai1, Christopher R. Cox2, Alex Clarke3, Akihiro Shimotake4,5, Takayuki Kikuchi6, Takeharu Kuneida6,7, Yoshiki Arakawa6, Ryosuke Takahashi4, Akio Ikeda8, Riki Matsumoto4,9, Timothy T. Rogers10, Matthew A. Lambon Ralph1
ORCID iDs: Saskia L. Frisby, Ajay D. Halai, Christopher R. Cox
  1. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
  2. Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
  3. Department of Psychology, University of Warwick, Warwick, United Kingdom
  4. Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
  5. Clinical Research Center, National Hospital Organization Utano Hospital, Kyoto, Japan
  6. Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
  7. Department of Neurosurgery, Ehime University Graduate School of Medicine, Ehime, Japan
  8. Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
  9. Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan
  10. Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
Institutions: MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); Louisiana State University (United States); University of Warwick (United Kingdom); Kyoto University (Japan); Utano Hospital (Japan); Ehime University (Japan); Kobe University (Japan); University of Wisconsin–Madison (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1201
Dates: received 2 July 2025; accepted 12 March 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1201 · PMID 42016557 · PMCID PMC13094022 · OpenAlex W7138947669
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism)
Methods: Preprocessing, Statistics, Machine learning, Smoothing, state filtering, decompositions, Spectral & time-frequency, fMRI & imaging, Physiology & signal measures, Connectivity
Keywords: electrocorticography, intracranial electrophysiology, time-frequency analysis, semantic representation, decoding, multivariate pattern analysis
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Medical Research Council (MR/V031481/1, MR/R023883/1, MC_UU_00005/18); Japan Society for the Promotion of Science (KAKENHI 23KK0146, KAKENHI 22H02945); European Research Council (GAP 670428-30 BRAIN2MIND_NEUROCOMP)
Citations: cited by 2 papers (Europe PMC); 116 references in the paper

Abstract

Intracranial electrophysiology offers a unique insight into the nature of information representation in the brain—it can be used to disentangle information encoded in gamma and high gamma frequencies from information encoded in lower frequencies. We used regularised logistic regression to decode animacy from time-frequency power and phase extracted from electrocorticography (ECoG) grid electrode data recorded on the surface of human ventral anterior temporal lobe (vATL). Power in gamma (30–60 Hz) and high gamma (60–200 Hz) produced reliable decoding, indicating that semantic information is, indeed, expressed by local populations in vATL. However, power from a wide range of frequencies (4–200 Hz) produced significantly higher decoding accuracy and also exhibited the same rapidly-changing dynamic code previously observed when decoding voltage. These findings support the theory that semantic information is encoded by a local vATL “hub” that interacts with distributed cortical “spokes”.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

slfrisby/ECoG_LASSO

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 64c12222a1581b0911a83d462cff4c99e69b5c6d, 24 March 2026
Languages: MATLAB (2155), CUDA (40), C++ (34), R (33), Shell (20), Python (14), Jupyter (8), C/C++ (5), C (3), JavaScript (3)
Size: 3,138 files, 2,315 scripts
Software Heritage: not archived
Found in: the text, “Preprocessing: ECoG”
Holds: README, environment (bids-matlab-main/requirements.txt, bids-matlab-main/binder/apt.txt, bids-matlab-main/binder/environment.yml, bids-matlab-main/binder/postBuild), tests, documentation, 8 notebooks
Not found: license file, CITATION.cff, continuous integration
Tools: EEGLAB (309 files), Parallel Computing Toolbox (38 files), Statistics and Machine Learning Toolbox (23 files), FieldTrip (14 files), Image Processing Toolbox (14 files), Signal Processing Toolbox (12 files), SPM (5 files), ICLabel (3 files), tidyverse (3 files), Optimization Toolbox (2 files), NumPy (2 files), Plotly (2 files), reshape2 (2 files), SciPy (2 files), BIDS Validator (1 file), Brainstorm (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
2,000 files
At the source: github.com/slfrisby/ECoG_LASSO/

eeglab.org

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Preprocessing: ECoG”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: eeglab.org/

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

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,999 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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

We are unable to share raw data for this study because the patients did not provide informed consent to do so. However, matrices containing power, phase, and voltage features (columns) for each stimulus (rows) are available at https://osf.io/m5v42/. Code is available at https://github.com/slfrisby/ECoG_LASSO/.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 13 authors, 6 keywords, 3 funders, 116 references.

Cite

This paper

Frisby, S. L., Halai, A. D., Cox, C. R., Clarke, A., Shimotake, A., Kikuchi, T., Kuneida, T., Arakawa, Y., Takahashi, R., Ikeda, A., Matsumoto, R., Rogers, T. T., & Lambon Ralph, M. A. (2026). All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1201. https://doi.org/10.1162/imag.a.1201

BibTeX

@article{frisby2026all,
author = {Frisby, Saskia L. and Halai, Ajay D. and Cox, Christopher R. and Clarke, Alex and Shimotake, Akihiro and Kikuchi, Takayuki and Kuneida, Takeharu and Arakawa, Yoshiki and Takahashi, Ryosuke and Ikeda, Akio and Matsumoto, Riki and Rogers, Timothy T. and Lambon Ralph, Matthew A.},
title = {{All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1201},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1201},
url = {https://doi.org/10.1162/imag.a.1201},
pmid = {42016557},
pmcid = {PMC13094022}
}

RIS

TY - JOUR
AU - Frisby, Saskia L.
AU - Halai, Ajay D.
AU - Cox, Christopher R.
AU - Clarke, Alex
AU - Shimotake, Akihiro
AU - Kikuchi, Takayuki
AU - Kuneida, Takeharu
AU - Arakawa, Yoshiki
AU - Takahashi, Ryosuke
AU - Ikeda, Akio
AU - Matsumoto, Riki
AU - Rogers, Timothy T.
AU - Lambon Ralph, Matthew A.
TI - All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/17
VL - 4
SP - IMAG.a.1201
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1201
UR - https://doi.org/10.1162/imag.a.1201
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex",
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{
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"family": "Ikeda",
"given": "Akio"
},
{
"family": "Matsumoto",
"given": "Riki"
},
{
"family": "Rogers",
"given": "Timothy T."
},
{
"family": "Lambon Ralph",
"given": "Matthew A."
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1201",
"DOI": "10.1162/imag.a.1201",
"PMID": "42016557",
"PMCID": "PMC13094022",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1201",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
17
]
]
}
}

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

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