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Does twitch-spindle coupling differ between N2 and N3 sleep in 6-month-olds?

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2 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 2 matches
  1. [1] § Methods › Data analysis › E‌EG signals ↔ fun_Spindle_Stats.m, lines 1–70 · score 0.90 · 12–14 Hz, median amplitude, filtered EEG, 11–15 Hz, detection, expanded
  2. [2] § Methods › Data analysis › Sleep spindles ↔ fun_Spindle_Stats.m, lines 1–70 · score 0.61 · spindles detected, sleep spindle frequency, Hilbert, signal, amplitude, waveform

Paper

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

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The authors' code

MATLAB · 160 lines · 7 KB · no license · 2 matches

  1. function Output = fun_Spindle_Stats(EEGValues,EEGTimes,EEGArtifact_yn, Fs, MinLength, LT, UT)
  2. % Output = Spindle_Stats(EEGValues,EEGTimes,EEGArtifact_yn, Fs, MinLength, LT, UT)
  3. %
  4. % Script that takes an EEG signal and calculates several sleep spindle
  5. % related numbers.
  6. %
  7. % Dependencies:
  8. % SpindleFilter.m Filters EEG to spindle frequency
  9. % Logical_Consecutive.m Determines the length of sleep spindles
  10. % Matlab Signal Processing Toolbox
  11. %
  12. % Inputs:
  13. % EEGValues 1xn Vector The value of the EEG waveform
  14. % EEGTimes 1xn vector The time of each EEG datapoint
  15. % Fs Number The sampling rate of the EEG waveform
  16. % MinLength Number The minimum length for a sleep spindle
  17. % in seconds
  18. % LT Number The lower amplitude threshold for detection
  19. % (ex 2x median amplitude LT=2)
  20. % UT Number The upper amplitude threshold for detection
  21. % (ex: toddlers McClain in 6x, adults 9x)
  22. %
  23. % Outputs:
  24. % Output A structure with the following fields:
  25. % .Values The filtered EEG waveform
  26. % .Times The Times of the filtered EEG waveform
  27. % .Amplitude The amplitude of the resulting waveform
  28. % .Phase The phase of the resulting waveform
  29. % .Threshold The threshold used on the Amplitude to determine a
  30. % sleep spindle.
  31. % .SpindleLogical A logical that reports whether each data point is during
  32. % a sleep spindle
  33. % .SpindleIndex The same as SpindleLogical, except instead of all
  34. % spindles being 1, they are an integer of what number
  35. % sleep spindle it is. Helps for indexing particular
  36. % sleep spindles.
  37. %
  38. % Given K sleep spindles detected
  39. %
  40. % .SB.StartTime 1xK vector The start time of the sleep spindle.
  41. % .SB.EndTime 1xK vector The end time of the sleep spindle.
  42. % .SB.Duration 1xK vector The duration of the sleep spindle.
  43. % .SB.Amp 1xK cell The amplitude of the sleep spindle at each
  44. % datapoint.
  45. % .SB.Phase 1xK cell The phase of the sleep spindle at each
  46. % datapoint.
  47. % .SB.medAmp 1xK vector The median amplitude of the sleep spindle.
  48. % .SB.maxAmp 1xK vector The maximum amplitude of the sleep spindle.
  49. % .SB.sumPhase 1xK vector The sum of all phase changes for the spindle
  50. % burst.
  51. % .SB.nCycles 1xK vector The number of cycles for each sleep spindle.
  52. %
  53. % Initally used in:
  54. % % Sokoloff G, Dooley JC, Glanz RM, Yen RY, Hickerson MM, Evans L, Laughlin
  55. % HM, Apfelbaum KS, and Blumberg MS (2021). Twitches emerge postnatally
  56. % during quiet sleep in human infants and are synchronized with sleep
  57. % spindles. Current Biology. https://doi.org/10.1016/j.cub.2021.05.038
  58. %
  59. % Last updated 6/16/2024 by Taylor Christiansen added custom ampltiude and
  60. % spindle length inputs and expanded frequency from 12-14 to 11-15 Hz
  61. %
  62. % Used in Christiansen et al., 2026
  66. [fValues,fTimes] = SpindleFilter(EEGValues,EEGTimes,Fs); % Filter the signal
  67. fValuesAmp = abs(hilbert(fValues)); % Amplitude
  68. fValuesPhase = angle(hilbert(fValues)); % Phase
  69. dPhase = diff(unwrap(fValuesPhase)); % difference in phase
  70. dPhase = [0 dPhase]; % Pad the vector
  72. %% Only include signal not during noise to calculate the median Amplitude
  73. for t = 1:length(fTimes) %loop through EEG data row
  74. Amp = fValuesAmp (t); % Amplitude at time t
  75. Art = EEGArtifact_yn (t); % Art y or n at time t
  76. if Art == 1 ; %if the amplitude is during artifact period
  77. fValuesAmp_clean(t) = NaN;
  78. else
  79. fValuesAmp_clean(t) = Amp;
  80. end
  81. end
  83. medAmp = nanmedian(fValuesAmp_clean);
  84. LowerThreshold = medAmp*LT; % Change to change threshold
  85. UpperThreshold = medAmp*UT;
  87. fValuesAmpLogical = fValuesAmp_clean > LowerThreshold;
  88. ThreshDuration = Logical_Consecutive(fValuesAmpLogical);
  89. MinDuration = round(Fs*MinLength);
  91. DurationLogical = ThreshDuration.ConsecutiveOutput < MinDuration;
  92. fValuesAmpLogical(DurationLogical & fValuesAmpLogical) = 0;
  94. ThreshDuration = Logical_Consecutive(fValuesAmpLogical);
  95. DurationLogical = ThreshDuration.ConsecutiveOutput < MinDuration;
  96. fValuesAmpLogical(DurationLogical & ~fValuesAmpLogical) = 1;
  98. ThreshDuration = Logical_Consecutive(fValuesAmpLogical);
  100. dfValuesAmpLogical = diff(fValuesAmpLogical);
  101. SSI_raw = find(dfValuesAmpLogical == 1); % Spindle Start Index
  102. SEI_raw = find(dfValuesAmpLogical == -1); % Spindle End Index
  104. nSpindles_raw = min([length(SEI_raw); length(SSI_raw)]);
  106. SSI = []; %initalize
  107. SEI = [];
  109. %Loop Through existing spindles
  110. for iSpindle = 1:nSpindles_raw
  111. Amp_loop = [];
  112. Amp_loop = fValuesAmp(SSI_raw(iSpindle):SEI_raw(iSpindle));
  113. st = SSI_raw (iSpindle);
  114. en = SEI_raw (iSpindle);
  115. % Check if any value in the event crosses the Upper Threshold
  116. if any(Amp_loop > UpperThreshold) % if at any point the amplitude of spindle crosses the UpperThreshold save it
  117. % If it does, include the entire event
  118. SSI = [SSI st];
  119. SEI = [SEI en];
  120. end
  121. end
  123. nSpindles = [];
  124. nSpindles = min([length(SEI); length(SSI)]);
  126. Output.Values = fValues;
  127. Output.Times = fTimes;
  128. Output.Amplitude = fValuesAmp_clean;
  129. Output.Phase = fValuesPhase;
  130. Output.LowerThreshold = LowerThreshold;
  131. Output.UpperThreshold = UpperThreshold;
  132. Output.SpindleLogical = fValuesAmpLogical;
  133. Output.SpindleIndex = zeros(size(fValuesAmpLogical));
  135. if nSpindles > 0
  136. Output.nSpindles = nSpindles;
  137. else
  138. Output.nSpindles = 0;
  139. end
  141. % Save the information for the identified sleep spindles
  142. for iSpindle = 1:nSpindles
  143. Output.SpindleIndex(SSI(iSpindle):SEI(iSpindle)) = iSpindle; % Create string for easy sleep spindle indexing
  144. Output.SB.StartTime(iSpindle) = SSI(iSpindle)/Fs; % Get sleep spindle start time (in seconds)
  145. Output.SB.EndTime(iSpindle) = SEI(iSpindle)/Fs; % Get sleep spindle end time (in seconds)
  146. Output.SB.Duration(iSpindle) = Output.SB.EndTime(iSpindle) - Output.SB.StartTime(iSpindle); % Get sleep spindle duration
  147. Output.SB.Amp{iSpindle} = fValuesAmp(SSI(iSpindle):SEI(iSpindle)); % Get sleep spindle amplitude throughout
  148. Output.SB.Phase{iSpindle} = fValuesPhase(SSI(iSpindle):SEI(iSpindle)); % Get sleep spindle phase throughout
  149. Output.SB.medAmp(iSpindle) = median(fValuesAmp(SSI(iSpindle):SEI(iSpindle))); % Get sleep spindle median amplitude
  150. Output.SB.maxAmp(iSpindle) = max(fValuesAmp(SSI(iSpindle):SEI(iSpindle))); % Get sleep spindle max amplitude
  151. Output.SB.sumPhase(iSpindle) = sum(dPhase(SSI(iSpindle):SEI(iSpindle))); % Get the phase duration of the sleep spindle
  152. Output.SB.nCycles(iSpindle) = Output.SB.sumPhase(iSpindle) / (2*pi); % Get the number of cycles of the sleep spindle
  153. Output.SB.frequencies(iSpindle)= Output.SB.nCycles(iSpindle)/Output.SB.Duration(iSpindle); %use the number of cycles and the duration of the spindle to calculate frequency
  154. end
  156. end

fun_Spindle_Stats.m at commit 0bcc813, no license · at the source

Overview

Authors: Taylor G Christiansen1, Greta Sokoloff1,2, Hailey C Long1, Olivia K Kopp1, Lydia K Karr1, Mark S Blumberg1,2
ORCID iDs: Greta Sokoloff, Mark S Blumberg
  1. Department of Psychological and Brain Sciences, University of Iowa, Psychological and Brain Sciences Building, 340 Iowa Avenue, Iowa City, IA 52242, United States
  2. Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, United States
Institutions: University of Iowa (United States)
Journal: Sleep, volume 49, issue 4, article zsaf410
Dates: received 23 July 2025; accepted 17 December 2025; published online 23 December 2025; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/sleep/zsaf410 · PMID 41432252 · PMCID PMC13089613 · OpenAlex W7117155390
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Single-unit activity, calcium imaging
Keywords: infancy, development, sleep spindles, myoclonic twitches, delta oscillations, sensorimotor system
MeSH: Sleep Stages*, Delta Rhythm, Electroencephalography, Female, Humans, Infant, Male, Polysomnography, Sleep, REM (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NICHD NIH HHS (R01 HD104616)
Citations: cited by 2 papers (Europe PMC); 62 references in the paper

Abstract

Twitches are discrete movements that characterize rapid eye movement (REM) sleep. However, recent work showed that twitches also occur during non-REM (NREM) sleep in human infants beginning around 3 months of age, a time when sleep spindles and the cortical delta rhythm are also emerging. Further, NREM twitches are coupled with sleep spindles, suggesting a unique contribution to sensorimotor development. Given that NREM sleep is composed of distinct substages, we investigated whether twitching and twitch-spindle coupling are differentially expressed during N2 and N3 sleep. In 6-month-old human infants (n = 21; 7 females), we recorded electroencephalogram, respiration, and video during daytime sleep. We found high-intensity twitching during N2 and REM but not N3 sleep. In contrast, sleep spindles exhibited similar temporal characteristics during N2 and N3. Also, despite differences in the intensity of twitching during N2 and N3, significant twitch-spindle coupling occurred in both stages. Finally, the rate of twitching was inversely related to delta power across NREM periods. These findings suggest that although twitching occurs during REM, N2, and N3 sleep at this age, its expression is compatible with some sleep components (e.g., rapid eye movements, sleep spindles) but not others (e.g., cortical delta), highlighting the continuing need to better understand the dynamic organization of sleep and its individual components in early development.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Blumberg-Lab/Chirstiansen-et-al-2026

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0bcc8132c37c9c998a2da668ab91ac012eead447, 17 December 2025
Languages: MATLAB (8)
Size: 9 files, 8 scripts
Software Heritage: not archived
Found in: “Deposit of material in a data repository”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (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/Blumberg-Lab/Chirstiansen-et-al-2026

Tracing map

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  • 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;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 9 MeSH terms, 1 funder, 59 references.

Cite

This paper

Christiansen, T. G., Sokoloff, G., Long, H. C., Kopp, O. K., Karr, L. K., & Blumberg, M. S. (2026). Does twitch-spindle coupling differ between N2 and N3 sleep in 6-month-olds? Sleep, 49(4), zsaf410. https://doi.org/10.1093/sleep/zsaf410

BibTeX

@article{christiansen2026does,
author = {Christiansen, Taylor G and Sokoloff, Greta and Long, Hailey C and Kopp, Olivia K and Karr, Lydia K and Blumberg, Mark S},
title = {{Does twitch-spindle coupling differ between N2 and N3 sleep in 6-month-olds?}},
journal = {Sleep},
year = {2026},
month = apr,
volume = {49},
number = {4},
pages = {zsaf410},
publisher = {Oxford University Press},
issn = {0161-8105},
doi = {10.1093/sleep/zsaf410},
url = {https://doi.org/10.1093/sleep/zsaf410},
pmid = {41432252},
pmcid = {PMC13089613}
}

RIS

TY - JOUR
AU - Christiansen, Taylor G
AU - Sokoloff, Greta
AU - Long, Hailey C
AU - Kopp, Olivia K
AU - Karr, Lydia K
AU - Blumberg, Mark S
TI - Does twitch-spindle coupling differ between N2 and N3 sleep in 6-month-olds?
T2 - Sleep
J2 - Sleep
PY - 2026
DA - 2026/04/01
VL - 49
IS - 4
SP - zsaf410
SN - 0161-8105
PB - Oxford University Press
DO - 10.1093/sleep/zsaf410
UR - https://doi.org/10.1093/sleep/zsaf410
LA - en
ER -

CSL-JSON

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